Gaussian Process Explained | Papers With Code

Gaussian Process

Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.

Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams

Latest Papers

PAPER	DATE
OFAI-UKP at [email protected]: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning Tristan Miller • Erik-Lân Do Dinh • Edwin Simpson • Iryna Gurevych	2020-08-03
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships Nina Kudryashova • Theoklitos Amvrosiadis • Nathalie Dupuy • Nathalie Rochefort • Arno Onken	2020-08-03
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression Yixiang Deng • Guang Lin • Xiu Yang	2020-08-03
Cold Posteriors and Aleatoric Uncertainty Ben Adlam • Jasper Snoek • Samuel L. Smith	2020-07-31
Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee • Samuel S. Schoenholz • Jeffrey Pennington • Ben Adlam • Lechao Xiao • Roman Novak • Jascha Sohl-Dickstein	2020-07-31
Random Forests for dependent data Arkajyoti Saha • Sumanta Basu • Abhirup Datta	2020-07-30
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification \| Santiago Toledo-Cortés • Melissa De La Pava • Oscar Perdómo • Fabio A. González	2020-07-29
Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment A. F. López-Lopera • D. Idier • J. Rohmer • F. Bachoc	2020-07-28
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation Romit Maulik • Themistoklis Botsas • Nesar Ramachandra • Lachlan Robert Mason • Indranil Pan	2020-07-23
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes Arthur Leroy • Pierre Latouche • Benjamin Guedj • Servane Gey	2020-07-21
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks Alexander Immer	2020-07-21
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil Matheus Henrique Dal Molin Ribeiro • Ramon Gomes da Silva • Viviana Cocco Mariani • Leandro dos Santos Coelho	2020-07-21
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization Yang Yang • Ke Deng • Michael Zhu	2020-07-20
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake Snell • Richard Zemel	2020-07-20
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach Yun Yuan • Qinzheng Wang • Xianfeng Terry Yang	2020-07-17
Ordinal Regression with Fenton-Wilkinson Order Statistics: A Case Study of an Orienteering Race Joonas Pääkkönen	2020-07-14
Highway Traffic State Estimation Using Physics Regularized Gaussian Process: Discretized Formulation Yun Yuan • Zhao Zhang • Xianfeng Terry Yang	2020-07-14
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes \| William J. Wilkinson • Paul E. Chang • Michael Riis Andersen • Arno Solin	2020-07-12
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis Zirui Li • Chao Lu • Cheng Gong • Cheng Gong • Jinghang Li • Lianzhen Wei	2020-07-11
Numerical simulation, clustering and prediction of multi-component polymer precipitation Pavan Inguva • Lachlan Mason • Indranil Pan • Miselle Hengardi • Omar K. Matar	2020-07-10
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers Tom Lovett • Mark Briers • Marcos Charalambides • Radka Jersakova • James Lomax • Chris Holmes	2020-07-09
Understanding the effect of hyperparameter optimization on machine learning models for structure design problems Xianping Du • Hongyi Xu • Feng Zhu	2020-07-04
Gaussian Process Regression with Local Explanation Yuya Yoshikawa • Tomoharu Iwata	2020-07-03
BOSH: Bayesian Optimization by Sampling Hierarchically Henry B. Moss • David S. Leslie • Paul Rayson	2020-07-02
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis Elena Raponi • Hao Wang • Mariusz Bujny • Simonetta Boria • Carola Doerr	2020-07-02
Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers Ridwan Alam • David B. Peden • John C. Lach	2020-07-02
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes Ali Hebbal • Loic Brevault • Mathieu Balesdent • El-Ghazali Talbi • Nouredine Melab	2020-06-29
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang • Daniel R. Jiang • Maximilian Balandat • Brian Karrer • Jacob R. Gardner • Roman Garnett	2020-06-29
Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process Liwei Wang • Siyu Tao • Ping Zhu • Wei Chen	2020-06-27
Prediction with Gaussian Process Dynamical Models Thomas Beckers • Sandra Hirche	2020-06-25
Epoch-evolving Gaussian Process Guided Learning Jiabao Cui • Xuewei Li • Bin Li • Hanbin Zhao • Bourahla Omar • Xi Li	2020-06-25
Heat kernel and intrinsic Gaussian processes on manifolds Ke Ye • Mu Niu • Pokman Cheung	2020-06-25
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization Antonio Candelieri • Riccardo Perego • Francesco Archetti	2020-06-25
Likelihood-Free Gaussian Process for Regression \| Yuta Shikuri	2020-06-24
Pareto Active Learning with Gaussian Processes and Adaptive Discretization Andi Nika • Kerem Bozgan • Çağın Ararat • Cem Tekin	2020-06-24
Variational Orthogonal Features David R. Burt • Carl Edward Rasmussen • Mark van der Wilk	2020-06-23
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors \| Yuling Yao • Aki Vehtari • Andrew Gelman	2020-06-22
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure Shogo Iwazaki • Yu Inatsu • Ichiro Takeuchi	2020-06-22
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes \| Mengdi Xu • Wenhao Ding • Jiacheng Zhu • Zuxin Liu • Baiming Chen • Ding Zhao	2020-06-19
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev • Henri Pesonen • Markus Heinonen • Jukka Corander • Samuel Kaski	2020-06-18
Exact posterior distributions of wide Bayesian neural networks Jiri Hron • Yasaman Bahri • Roman Novak • Jeffrey Pennington • Jascha Sohl-Dickstein	2020-06-18
GPIRT: A Gaussian Process Model for Item Response Theory JBrandon Duck-Mayr • Roman Garnett • Jacob M. Montgomery	2020-06-17
Longitudinal Variational Autoencoder Siddharth Ramchandran • Gleb Tikhonov • Miika Koskinen • Harri Lähdesmäki	2020-06-17
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu • Zi Lin • Shreyas Padhy • Dustin Tran • Tania Bedrax-Weiss • Balaji Lakshminarayanan	2020-06-17
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Laura Swiler • Mamikon Gulian • Ari Frankel • Cosmin Safta • John Jakeman	2020-06-16
Real-Time Regression with Dividing Local Gaussian Processes Armin Lederer • Alejandro Jose Ordonez Conejo • Korbinian Maier • Wenxin Xiao • Sandra Hirche	2020-06-16
Sparse Gaussian Process Based On Hat Basis Functions Wenqi Fang • Huiyun Li • Hui Huang • Shaobo Dang • Zhejun Huang • Zheng Wang	2020-06-15
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi • Felix Berkenkamp • Andreas Krause	2020-06-15
Variational Bayesian Monte Carlo with Noisy Likelihoods \| Luigi Acerbi	2020-06-15
GP3: A Sampling-based Analysis Framework for Gaussian Processes Armin Lederer • Markus Kessler • Sandra Hirche	2020-06-14
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression Wenxin Xiao • Armin Lederer • Sandra Hirche	2020-06-14
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search Vu Nguyen • Tam Le • Makoto Yamada • Michael A Osborne	2020-06-13
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel Binxin Ru • Xingchen Wan • Xiaowen Dong • Michael Osborne	2020-06-13
Gaussian Processes on Graphs via Spectral Kernel Learning Yin-Cong Zhi • Yin Cheng Ng • Xiaowen Dong	2020-06-12
Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamás Erdélyi • Cameron Musco • Christopher Musco	2020-06-12
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher T. Jensen • Ta-Chu Kao • Marco Tripodi • Guillaume Hennequin	2020-06-12
SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers • Daniel Palenicek • Vincent Moens • Mohammed Abdullah • Aivar Sootla • Jun Wang • Haitham Ammar	2020-06-12
Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis MohammadReza Ebrahimi • Navona Calarco • Kieran Campbell • Colin Hawco • Aristotle Voineskos • Ashish Khisti	2020-06-10
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model Tianjian Zhou • Yuan Ji	2020-06-10
Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor • Theofanis Karaletsos • Thang D. Bui	2020-06-09
Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu • Xing Liu • Ruya Kang • Zhichao Shen • Seth Flaxman • François-Xavier Briol	2020-06-09
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili • Victor Picheny • Artem Artemev	2020-06-09
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder Alex Campbell • Pietro Liò	2020-06-08
Schrödinger PCA: You Only Need Variances for Eigenmodes Ziming Liu • Sitian Qian • Yixuan Wang • Yuxuan Yan • Tianyi Yang	2020-06-08
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation \| Julian Berk • Sunil Gupta • Santu Rana • Svetha Venkatesh	2020-06-08
Physics Regularized Gaussian Processes Zheng Wang • Wei Xing • Robert Kirby • Shandian Zhe	2020-06-08
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation Zheng Wang • Wei Xing • Robert Kirby • Shandian Zhe	2020-06-08
Learning compositional models of robot skills for task and motion planning Zi Wang • Caelan Reed Garrett • Leslie Pack Kaelbling • Tomás Lozano-Pérez	2020-06-08
A conditional one-output likelihood formulation for multitask Gaussian processes Vanessa Gómez-Verdejo • Óscar García-Hinde • Manel Martínez-Ramón	2020-06-05
Health Indicator Forecasting for Improving Remaining Useful Life Estimation Qiyao Wang • Ahmed Farahat • Chetan Gupta • Haiyan Wang	2020-06-05
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels Jarred Barber	2020-06-05
Quadruply Stochastic Gaussian Processes Trefor W. Evans • Prasanth B. Nair	2020-06-04
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules Michele Fraccaroli • Evelina Lamma • Fabrizio Riguzzi	2020-06-03
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels Marcus M. Noack • Gregory S. Doerk • Ruipeng Li • Jason K. Streit • Richard A. Vaia • Kevin G. Yager • Masafumi Fukuto	2020-06-03
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models Jwala Dhamala • John L. Sapp • B. Milan Horácek • Linwei Wang	2020-06-02
Semi-supervised deep learning for high-dimensional uncertainty quantification Zequn Wang • Mingyang Li	2020-06-01
GP-NAS: Gaussian Process Based Neural Architecture Search Zhihang Li • Teng Xi • Jiankang Deng • Gang Zhang • Shengzhao Wen • Ran He	2020-06-01
Video Instance Segmentation Tracking With a Modified VAE Architecture Chung-Ching Lin • Ying Hung • Rogerio Feris • Linglin He	2020-06-01
Bayesian Restoration of Audio Degraded by Low-Frequency Pulses Modeled via Gaussian Process Hugo Tremonte de Carvalho • Flávio Rainho Ávila • Luiz Wagner Pereira Biscainho	2020-05-28
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks Jinglin Zhang • Wenjun Xu • Hui Gao • Miao Pan • Zhu Han • Ping Zhang	2020-05-28
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure \| Koorosh Aslansefat • Ioannis Sorokos • Declan Whiting • Ramin Tavakoli Kolagari • Yiannis Papadopoulos # 1 CLASSIFICATION ON XOR	2020-05-27
How Training Data Impacts Performance in Learning-based Control Armin Lederer • Alexandre Capone • Jonas Umlauft • Sandra Hirche	2020-05-25
Path Imputation Strategies for Signature Models of Irregular Time Series Michael Moor • Max Horn • Christian Bock • Karsten Borgwardt • Bastian Rieck	2020-05-25
Longitudinal Deep Kernel Gaussian Process Regression Junjie Liang • Yanting Wu • Dongkuan Xu • Vasant Honavar	2020-05-24
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation Yifan Chen • Houman Owhadi • Andrew M. Stuart	2020-05-22
Data-driven Efficient Solvers and Predictions of Conformational Transitions for Langevin Dynamics on Manifold in High Dimensions Yuan Gao • Jian-Guo Liu • Nan Wu	2020-05-22
Global Optimization of Gaussian processes Artur M. Schweidtmann • Dominik Bongartz • Daniel Grothe • Tim Kerkenhoff • Xiaopeng Lin • Jaromil Najman • Alexander Mitsos	2020-05-21
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide Kyle P Messier • Matthias Katzfuss	2020-05-19
Deep Latent-Variable Kernel Learning \| Haitao Liu • Yew-Soon Ong • Xiaomo Jiang • Xiaofang Wang	2020-05-18
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes Lai Wei • Xiaobo Tan • Vaibhav Srivastava	2020-05-18
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober • Laurence Aitchison	2020-05-17
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes Zhaozhi Qian • Ahmed M. Alaa • Mihaela van der Schaar	2020-05-13
Parameter Inference for Weak Lensing using Gaussian Processes and MOPED Arrykrishna Mootoovaloo • Alan F. Heavens • Andrew H. Jaffe • Florent Leclercq	2020-05-13
Machine learning based digital twin for dynamical systems with multiple time-scales Souvik Chakraborty • Sondipon Adhikari	2020-05-12
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS Shubhanshu Shekhar • Tara Javidi	2020-05-11
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design Rémy Priem • Nathalie Bartoli • Youssef Diouane • Alessandro Sgueglia	2020-05-11
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires Moonseop Kim • Huayi Yin • Guang Lin	2020-05-11
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques Moajjem Hossain Chowdhury • Md Nazmul Islam Shuzan • Muhammad E. H. Chowdhury • Zaid B Mahbub • M. Monir Uddin • Amith Khandakar • Mamun Bin Ibne Reaz	2020-05-07
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition Steven I Reeves • Dongwook Lee • Anurag Singh • Kunal Verma	2020-05-07
Planning from Images with Deep Latent Gaussian Process Dynamics Nathanael Bosch • Jan Achterhold • Laura Leal-Taixé • Jörg Stückler	2020-05-07
Active Preference-Based Gaussian Process Regression for Reward Learning \| Erdem Bıyık • Nicolas Huynh • Mykel J. Kochenderfer • Dorsa Sadigh	2020-05-06
Using Machine Learning to Emulate Agent-Based Simulations Claudio Angione • Eric Silverman • Elisabeth Yaneske	2020-05-05
Localized active learning of Gaussian process state space models Alexandre Capone • Jonas Umlauft • Thomas Beckers • Armin Lederer • Sandra Hirche	2020-05-04
Evaluation of Deep Gaussian Processes for Text Classification P. Jayashree • P. K. Srijith	2020-05-01
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition Raul Quintero • Ignacio Parra • David Fernandez Llorca • Miguel Angel Sotelo	2020-04-30
Continual Deep Learning by Functional Regularisation of Memorable Past Pingbo Pan • Siddharth Swaroop • Alexander Immer • Runa Eschenhagen • Richard E. Turner • Mohammad Emtiyaz Khan	2020-04-29
Guided search for desired functional responses via Bayesian optimization of generative model: hysteresis loop shape engineering in ferroelectrics Sergei V. Kalinin • Maxim Ziatdinov • Rama K. Vasudevan	2020-04-27
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage Xiaowei Yue • Yuchen Wen • Jeffrey H. Hunt • Jianjun Shi	2020-04-23
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck Alec Koppel • Hrusikesha Pradhan • Ketan Rajawat	2020-04-23
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes \| A. Rene Geist • Sebastian Trimpe	2020-04-23
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity Sam Coveney • Cesare Corrado • Caroline H Roney • Daniel O'Hare • Steven E Williams • Mark D O'Neill • Steven A Niederer • Richard H Clayton • Jeremy E Oakley • Richard D Wilkinson	2020-04-22
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit Tomoki Koriyama • Hiroshi Saruwatari	2020-04-22
Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework Sandrine Gümbel • Thorsten Schmidt	2020-04-16
Gaussian Process Learning-based Probabilistic Optimal Power Flow Parikshit Pareek • Hung D. Nguyen	2020-04-16
Foreground modelling via Gaussian process regression: an application to HERA data Abhik Ghosh • Florent Mertens • Gianni Bernardi • Mário G. Santos • Nicholas S. Kern • Christopher L. Carilli • Trienko L. Grobler • Léon V. E. Koopmans • Daniel C. Jacobs • Adrian Liu • Aaron R. Parsons • Miguel F. Morales • James E. Aguirre • Joshua S. Dillon • Bryna J. Hazelton • Oleg M. Smirnov • Bharat K. Gehlot • Siyanda Matika • Paul Alexander • Zaki S. Ali • Adam P. Beardsley • Roshan K. Benefo • Tashalee S. Billings • Judd D. Bowman • Richard F. Bradley • Carina Cheng • Paul M. Chichura • David R. DeBoer • Eloy de Lera Acedo • Aaron Ewall-Wice • Gcobisa Fadana • Nicolas Fagnoni • Austin F. Fortino • Randall Fritz • Steve R. Furlanetto • Samavarti Gallardo • Brian Glendenning • Deepthi Gorthi • Bradley Greig • Jasper Grobbelaar • Jack Hickish • Alec Josaitis • Austin Julius • Amy S. Igarashi • MacCalvin Kariseb • Saul A. Kohn • Matthew Kolopanis • Telalo Lekalake • Anita Loots • David MacMahon • Lourence Malan • Cresshim Malgas • Matthys Maree • Zachary E. Martinot • Nathan Mathison • Eunice Matsetela • Andrei Mesinger • Abraham R. Neben • Bojan Nikolic • Chuneeta D. Nunhokee • Nipanjana Patra • Samantha Pieterse • Nima Razavi-Ghods • Jon Ringuette • James Robnett • Kathryn Rosie • Raddwine Sell • Craig Smith • Angelo Syce • Max Tegmark • Nithyanandan Thyagarajan • Peter K. G. Williams • Haoxuan Zheng	2020-04-13
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties \| Richard Cheng • Mohammad Javad Khojasteh • Aaron D. Ames • Joel W. Burdick	2020-04-11
Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation Sergei V. Kalinin • Mani Valleti • Rama K. Vasudevan • Maxim Ziatdinov	2020-04-09
Online Constrained Model-based Reinforcement Learning Benjamin van Niekerk • Andreas Damianou • Benjamin Rosman	2020-04-07
Direct loss minimization algorithms for Bayesian predictors Yadi Wei • Rishit Sheth • Roni Khardon	2020-04-07
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes Moyan Li • Raed Kontar	2020-04-06
Gaussian Process Boosting \| Fabio Sigrist	2020-04-06
Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction Constantinos Daskalakis • Petros Dellaportas • Aristeidis Panos	2020-04-03
Predicting the outputs of finite networks trained with noisy gradients Gadi Naveh • Oded Ben-David • Haim Sompolinsky • Zohar Ringel	2020-04-02
Projection Pursuit Gaussian Process Regression Gecheng Chen • Rui Tuo	2020-04-01
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data Leif Erik Andersson • Bart Doekemeijer • Daan van der Hoek • Jan-Willem van Wingerden • Lars Imsland	2020-03-30
Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory Ali Reza Pedram • Takashi Tanaka	2020-03-27
On Infinite-Width Hypernetworks Etai Littwin • Tomer Galanti • Lior Wolf • Greg Yang	2020-03-27
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles Max Veit • David M. Wilkins • Yang Yang • Robert A. DiStasio Jr. • Michele Ceriotti	2020-03-27
Preferential Batch Bayesian Optimization Eero Siivola • Akash Kumar Dhaka • Michael Riis Andersen • Javier Gonzalez • Pablo Garcia Moreno • Aki Vehtari	2020-03-25
Efficient Gaussian Process Bandits by Believing only Informative Actions Amrit Singh Bedi • Dheeraj Peddireddy • Vaneet Aggarwal • Alec Koppel	2020-03-23
Analysis of Greenhouse Gases Shalin Shah	2020-03-21
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation Xiong Lyu • Mike Ludkovski	2020-03-19
Gaze-Sensing LEDs for Head Mounted Displays Kaan Akşit • Jan Kautz • David Luebke	2020-03-18
The Elliptical Processes: a New Family of Flexible Stochastic Processes Maria Bånkestad • Jens Sjölund • Jalil Taghia • Thomas Schön	2020-03-13
Data-driven surrogate modelling and benchmarking for process equipment Gabriel F. N. Gonçalves • Assen Batchvarov • Yuyi Liu • Yuxin Liu • Lachlan Mason • Indranil Pan • Omar K. Matar	2020-03-13
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time Hideaki Imamura • Nontawat Charoenphakdee • Futoshi Futami • Issei Sato • Junya Honda • Masashi Sugiyama	2020-03-10
Channel Attention with Embedding Gaussian Process: A Probabilistic Methodology Jiyang Xie • Dongliang Chang • Zhanyu Ma • Guoqiang Zhang • Jun Guo	2020-03-10
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization Antonio Candelieri • Ilaria Giordani • Riccardo Perego • Francesco Archetti	2020-03-09
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Feng Yin • Zhidi Lin • Yue Xu • Qinglei Kong • Deshi Li • Sergios Theodoridis • Shuguang • Cui	2020-03-08
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi • Markus Heinonen • Edwin V. Bonilla • Zheyang Shen • Maurizio Filippone	2020-03-06
Corruption-Tolerant Gaussian Process Bandit Optimization Ilija Bogunovic • Andreas Krause • Jonathan Scarlett	2020-03-04
Regression via Implicit Models and Optimal Transport Cost Minimization \| Saurav Manchanda • Khoa Doan • Pranjul Yadav • S. Sathiya Keerthi	2020-03-03
Gaussian Process Policy Optimization Ashish Rao • Bidipta Sarkar • Tejas Narayanan	2020-03-02
Imbalance Learning for Variable Star Classification Zafiirah Hosenie • Robert Lyon • Benjamin Stappers • Arrykrishna Mootoovaloo • Vanessa McBride	2020-02-27
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients Sébastien Petit • Julien Bect • Sébastien da Veiga • Paul Feliot • Emmanuel Vazquez	2020-02-26
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements Alberto Dalla Libera • Diego Romeres • Devesh K. Jha • Bill Yerazunis • Daniel Nikovski	2020-02-25
Generalized Bayesian Filtering via Sequential Monte Carlo Ayman Boustati • Ömer Deniz Akyildiz • Theodoros Damoulas • Adam Johansen	2020-02-23
Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast Fatemeh Najibi • Dimitra Apostolopoulou • Eduardo Alonso	2020-02-23
Nonmyopic Gaussian Process Optimization with Macro-Actions Dmitrii Kharkovskii • Chun Kai Ling • Kian Hsiang Low	2020-02-22
Efficiently Sampling Functions from Gaussian Process Posteriors \| James T. Wilson • Viacheslav Borovitskiy • Alexander Terenin • Peter Mostowsky • Marc Peter Deisenroth	2020-02-21
Development of modeling and control strategies for an approximated Gaussian process Shisheng Cui • Chia-Jung Chang	2020-02-12
Regret Bounds for Noise-Free Bayesian Optimization Sattar Vakili • Victor Picheny • Nicolas Durrande	2020-02-12
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data Johannes A. Stork • Todor Stoyanov	2020-02-12
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation Subhayan De • Jolene Britton • Matthew Reynolds • Ryan Skinner • Kenneth Jansen • Alireza Doostan	2020-02-11
Graph Convolutional Gaussian Processes For Link Prediction Felix L. Opolka • Pietro Liò	2020-02-11
Gaussian process imputation of multiple financial series Taco de Wolff • Alejandro Cuevas • Felipe Tobar	2020-02-11
Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems Xiaoran Ruan • Ke Li • Bilel Derbel • Arnaud Liefooghe	2020-02-08
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications Yun Yuan • Xianfeng Terry Yang • Zhao Zhang • Shandian Zhe	2020-02-06
One-Shot Bayes Opt with Probabilistic Population Based Training Jack Parker-Holder • Vu Nguyen • Stephen Roberts	2020-02-06
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction Andrew Patterson • Aditya Gahlawat • Naira Hovakimyan	2020-02-05
Uncertainty Quantification for Bayesian Optimization Rui Tuo • Wenjia Wang	2020-02-04
Linearly Constrained Gaussian Processes with Boundary Conditions Markus Lange-Hegermann	2020-02-03
A memory of motion for visual predictive control tasks Antonio Paolillo • Teguh Santoso Lembono • Sylvain Calinon	2020-01-31
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions Toni Karvonen • George Wynne • Filip Tronarp • Chris J. Oates • Simo Särkkä	2020-01-29
Convergence Guarantees for Gaussian Process Means with Misspecified Likelihoods and Smoothness George Wynne • François-Xavier Briol • Mark Girolami	2020-01-29
TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model Yusuke Uchiyama • Kei Nakagawa	2020-01-29
Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption Peter Fenner • Edward O. Pyzer-Knapp	2020-01-28
Bayesian nonparametric shared multi-sequence time series segmentation Olga Mikheeva • Ieva Kazlauskaite • Hedvig Kjellström • Carl Henrik Ek	2020-01-27
Bayesian optimization for backpropagation in Monte-Carlo tree search Yueqin Li • Nengli Lim	2020-01-25
The role of surrogate models in the development of digital twins of dynamic systems Souvik Chakraborty • Sondipon Adhikari • Ranjan Ganguli	2020-01-25
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes Daniele Gammelli • Inon Peled • Filipe Rodrigues • Dario Pacino • Haci A. Kurtaran • Francisco C. Pereira	2020-01-21
Projection based Active Gaussian Process Regression for Pareto Front Modeling Zhengqi Gao • Jun Tao • Yangfeng Su • Dian Zhou • Xuan Zeng	2020-01-20
Scalable Hyperparameter Optimization with Lazy Gaussian Processes \| Raju Ram • Sabine Müller • Franz-Josef Pfreundt • Nicolas R. Gauger • Janis Keuper	2020-01-16
Doubly Sparse Variational Gaussian Processes Vincent Adam • Stefanos Eleftheriadis • Nicolas Durrande • Artem Artemev • James Hensman	2020-01-15
Robust Gaussian Process Regression with a Bias Model Chiwoo Park • David J. Borth • Nicholas S. Wilson • Chad N. Hunter • Fritz J. Friedersdorf	2020-01-14
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process Siming Bayer • Ute Spiske • Jie Luo • Tobias Geimer • William M. Wells III • Martin Ostermeier • Rebecca Fahrig • Arya Nabavi • Christoph Bert • Ilker Eyupoglo • Andreas Maier	2020-01-12
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering Yohan Jung • Jinkyoo Park	2020-01-07
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes Devanshu Agrawal • Theodore Papamarkou • Jacob Hinkle	2020-01-03
Antifragile and Robust Heteroscedastic Bayesian Optimisation Anonymous	2020-01-01
Learning Curves for Deep Neural Networks: A field theory perspective Anonymous	2020-01-01
Attentive Sequential Neural Processes Anonymous	2020-01-01
Probabilistic modeling the hidden layers of deep neural networks Anonymous	2020-01-01
Knockoff-Inspired Feature Selection via Generative Models Anonymous	2020-01-01
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search Anonymous	2020-01-01
Learning Human Postural Control with Hierarchical Acquisition Functions Nils Rottmann • Tjasa Kunavar • Jan Babic • Jan Peters • Elmar Rueckert	2020-01-01
Learning Neural Surrogate Model for Warm-Starting Bayesian Optimization Haotian Zhang • Jian Sun • Zongben Xu	2020-01-01
Approximate Inference for Fully Bayesian Gaussian Process Regression Vidhi Lalchand • Carl Edward Rasmussen	2019-12-31
Disentangling Trainability and Generalization in Deep Neural Networks Lechao Xiao • Jeffrey Pennington • Samuel S. Schoenholz	2019-12-30
A statistical test for correspondence of texts to the Zipf-Mandelbrot law Anik Chakrabarty • Mikhail Chebunin • Artyom Kovalevskii • Ilya Pupyshev • Natalia Zakrevskaya • Qianqian Zhou	2019-12-25
Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning Alireza Hajipour • Arash Mirabdolah Lavasani • Mohammad Eftekhari Yazdi • Amir Mosavi • Shahaboddin Shamshirband • Kwok-Wing Chau	2019-12-25
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing • Elena Arcari • Lukas P. Fröhlich • Melanie N. Zeilinger	2019-12-23
Tensor Basis Gaussian Process Models of Hyperelastic Materials Ari Frankel • Reese Jones • Laura Swiler	2019-12-23
Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang • Christian J. Walder • Edwin V. Bonilla • Marian-Andrei Rizoiu • Lexing Xie	2019-12-21
Methods for comparing uncertainty quantifications for material property predictions Kevin Tran • Willie Neiswanger • Junwoong Yoon • Eric Xing • Zachary W. Ulissi	2019-12-20
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems \| Wei Huang • Richard Yi Da Xu	2019-12-19
Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures \| Taehee Lee • Charles E. Lawrence	2019-12-18
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design Piyush Pandita • Nimish Awalgaonkar • Ilias Bilionis • Jitesh Panchal	2019-12-16
Active emulation of computer codes with Gaussian processes -- Application to remote sensing Daniel Heestermans Svendsen • Luca Martino • Gustau Camps-Valls	2019-12-13
On the relationship between multitask neural networks and multitask Gaussian Processes Karthikeyan K • Shubham Kumar Bharti • Piyush Rai	2019-12-12
Learning and Optimization with Bayesian Hybrid Models Elvis A. Eugene • Xian Gao • Alexander W. Dowling	2019-12-12
Tensor Completion via Gaussian Process Based Initialization Yermek Kapushev • Ivan Oseledets • Evgeny Burnaev	2019-12-11
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax Daniel T. Chang	2019-12-11
Frequentist Consistency of Generalized Variational Inference Jeremias Knoblauch	2019-12-10
Location Trace Privacy Under Conditional Priors Casey Meehan • Kamalika Chaudhuri	2019-12-09
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies \| Juho Timonen • Henrik Mannerström • Aki Vehtari • Harri Lähdesmäki	2019-12-07
Ordinal Bayesian Optimisation Victor Picheny • Sattar Vakili • Artem Artemev	2019-12-05
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression Tong Teng • Jie Chen • Yehong Zhang • Kian Hsiang Low	2019-12-05
Numerical Gaussian process Kalman filtering Armin Küper • Steffen Waldherr	2019-12-03
Nonnegative Gaussian process tomography for generalized segmented planar detectors D. Blyth • N. Mullins • E. Galyaev • J. Holmes	2019-12-02
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes Rui Li	2019-12-01
Infra-slow brain dynamics as a marker for cognitive function and decline Shagun Ajmera Shyam Sunder Ajmera • Shreya Rajagopal • Razi Rehman • Devarajan Sridharan	2019-12-01
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes \| Lingge Li • Dustin Pluta • Babak Shahbaba • Norbert Fortin • Hernando Ombao • Pierre Baldi	2019-12-01
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes \| Greg Yang	2019-12-01
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network Shuhan Zhang • Wenlong Lyu • Fan Yang • Changhao Yan • Dian Zhou • Xuan Zeng	2019-12-01
Richer priors for infinitely wide multi-layer perceptrons Russell Tsuchida • Fred Roosta • Marcus Gallagher	2019-11-29
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach Mohamed K. Abdel-Aziz • Sumudu Samarakoon • Mehdi Bennis • Walid Saad	2019-11-27
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling Maxim Ziatdinov • Dohyung Kim • Sabine Neumayer • Rama K. Vasudevan • Liam Collins • Stephen Jesse • Mahshid Ahmadi • Sergei V. Kalinin	2019-11-26
Actively Learning Gaussian Process Dynamics Mona Buisson-Fenet • Friedrich Solowjow • Sebastian Trimpe	2019-11-22
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model Juan-José Giraldo • Mauricio A. Álvarez	2019-11-22
Towards a complete 3D morphable model of the human head \| Stylianos Ploumpis • Evangelos Ververas • Eimear O' Sullivan • Stylianos Moschoglou • Haoyang Wang • Nick Pears • William A. P. Smith • Baris Gecer • Stefanos Zafeiriou	2019-11-18
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference Leen Alawieh • Jonathan Goodman • John B. Bell	2019-11-17
Causal inference using Bayesian non-parametric quasi-experimental design \| Max Hinne • Marcel A. J. van Gerven • Luca Ambrogioni	2019-11-15
Conjugate Gradients for Kernel Machines Simon Bartels • Philipp Hennig	2019-11-14
Uncertainty on Asynchronous Time Event Prediction Marin Biloš • Bertrand Charpentier • Stephan Günnemann	2019-11-13
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness Zhu Li • Adrian Perez-Suay • Gustau Camps-Valls • Dino Sejdinovic	2019-11-11
Bayesian Active Learning for Structured Output Design Kota Matsui • Shunya Kusakawa • Keisuke Ando • Kentaro Kutsukake • Toru Ujihara • Ichiro Takeuchi	2019-11-09
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees Michael Maiworm • Daniel Limon • Rolf Findeisen	2019-11-08
Non-parametric Probabilistic Load Flow using Gaussian Process Learning Parikshit Pareek • Chuan Wang • Hung D. Nguyen	2019-11-08
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models Pavel Berkovich • Eric Perim • Wessel Bruinsma	2019-11-05
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data Akitoshi Masuda • Yoshihiko Susuki • Manel Martínez-Ramón • Andrea Mammoli • Atsushi Ishigame	2019-11-04
On Batch Bayesian Optimization Sayak Ray Chowdhury • Aditya Gopalan	2019-11-04
Seasonally-Adjusted Auto-Regression of Vector Time Series Enzo Busseti	2019-11-04
Online tuning and light source control using a physics-informed Gaussian process Adi A. Hanuka • J. Duris • J. Shtalenkova • D. Kennedy • A. Edelen • D. Ratner • X. Huang	2019-11-04
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li • Ruosong Wang • Dingli Yu • Simon S. Du • Wei Hu • Ruslan Salakhutdinov • Sanjeev Arora	2019-11-03
Continual Multi-task Gaussian Processes \| Pablo Moreno-Muñoz • Antonio Artés-Rodríguez • Mauricio A. Álvarez	2019-10-31
Safe Exploration for Interactive Machine Learning Matteo Turchetta • Felix Berkenkamp • Andreas Krause	2019-10-30
Function-Space Distributions over Kernels \| Gregory W. Benton • Wesley J. Maddox • Jayson P. Salkey • Julio Albinati • Andrew Gordon Wilson	2019-10-29
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes \| Greg Yang	2019-10-28
Convolutional Sequence Generation for Skeleton-Based Action Synthesis \| Sijie Yan • Zhizhong Li • Yuanjun Xiong • Huahan Yan	2019-10-27
Implicit Posterior Variational Inference for Deep Gaussian Processes \| Haibin Yu • Yizhou Chen • Zhongxiang Dai • Kian Hsiang Low • Patrick Jaillet	2019-10-26
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search \| Colin White • Willie Neiswanger • Yash Savani	2019-10-25
Sparse Orthogonal Variational Inference for Gaussian Processes \| Jiaxin Shi • Michalis K. Titsias • Andriy Mnih	2019-10-23
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform Alexander Schaefer • Lukas Luft • Wolfram Burgard	2019-10-23
Generalised learning of time-series: Ornstein-Uhlenbeck processes Mehmet Süzen • Alper Yegenoglu	2019-10-21
Bayesian Optimization Allowing for Common Random Numbers Michael Pearce • Matthias Poloczek • Juergen Branke	2019-10-21
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison	2019-10-17
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation Ryan-Rhys Griffiths • Miguel Garcia-Ortegon • Alexander A. Aldrick • Alpha A. Lee	2019-10-17
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics Chirag Modi • Uros Seljak	2019-10-16
Parametric Gaussian Process Regressors Martin Jankowiak • Geoff Pleiss • Jacob R. Gardner	2019-10-16
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue • Raed Kontar	2019-10-15
Regularized Sparse Gaussian Processes Rui Meng • Herbert Lee • Soper Braden • Priyadip Ray	2019-10-13
Bayesian Optimization using Pseudo-Points Chao Qian • Hang Xiong • Ke Xue	2019-10-12
Evolving Gaussian Process kernels from elementary mathematical expressions Ibai Roman • Roberto Santana • Alexander Mendiburu • Jose A. Lozano	2019-10-11
Learning from demonstration with model-based Gaussian process Noémie Jaquier • David Ginsbourger • Sylvain Calinon	2019-10-11
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks Julius von Kügelgen • Paul K Rubenstein • Bernhard Schölkopf • Adrian Weller	2019-10-09
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression Tong Ma • Renke Huang • David Barajas-Solano • Ramakrishna Tipireddy • Alexandre M. Tartakovsky	2019-10-09
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression Weiyang Zhang • Wenshuo Wang • Ding Zhao	2019-10-08
mfEGRA: Multifidelity Efficient Global Reliability Analysis Anirban Chaudhuri • Alexandre N. Marques • Karen E. Willcox	2019-10-06
Operational Calibration: Debugging Confidence Errors for DNNs in the Field Zenan Li • Xiaoxing Ma • Chang Xu • Jingwei Xu • Chun Cao • Jian Lü	2019-10-06
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning Sahand Rezaei-Shoshtari • David Meger • Inna Sharf	2019-10-05
Dynamic Embedding on Textual Networks via a Gaussian Process \| Pengyu Cheng • Yitong Li • Xinyuan Zhang • Liqun Cheng • David Carlson • Lawrence Carin	2019-10-05
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables Yichi Zhang • Daniel Apley • Wei Chen	2019-10-03
Kepler data analysis: non-Gaussian noise and Fourier Gaussian process analysis of star variability Jakob Robnik • Uroš Seljak	2019-10-02
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis Margherita Rosnati • Vincent Fortuin	2019-09-27
Deep recurrent Gaussian process with variational Sparse Spectrum approximation Roman Föll • Bernard Haasdonk • Markus Hanselmann • Holger Ulmer	2019-09-27
Debiased Bayesian inference for average treatment effects Kolyan Ray • Botond Szabo	2019-09-26
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario Thanh Le • Vasant Honavar	2019-09-25
Adversarial Vulnerability Bounds for Gaussian Process Classification Michael Thomas Smith • Kathrin Grosse • Michael Backes • Mauricio A Alvarez	2019-09-19
Bayesian Optimization under Heavy-tailed Payoffs Sayak Ray Chowdhury • Aditya Gopalan	2019-09-16
MCTS-based Automated Negotiation Agent Cédric Buron • Zahia Guessoum • Sylvain Ductor	2019-09-12
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning Arpan Kusari • Jonathan P. How	2019-09-11
Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model Yohei Sawada	2019-09-09
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images Nutan Chen • Göran Westling • Benoni B. Edin • Patrick van der Smagt	2019-09-09
Unsupervised Image Regression for Heterogeneous Change Detection Luigi T. Luppino • Filippo M. Bianchi • Gabriele Moser • Stian N. Anfinsen	2019-09-07
Deep kernel learning for integral measurements Carl Jidling • Johannes Hendriks • Thomas B. Schön • Adrian Wills	2019-09-04
Latent Gaussian process with composite likelihoods for data-driven disease stratification Siddharth Ramchandran • Miika Koskinen • Harri Lähdesmäki	2019-09-04
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning Lixue Cheng • Nikola B. Kovachki • Matthew Welborn • Thomas F. Miller III	2019-09-04
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis Ziqi Wang • Marco Broccardo	2019-08-27
Finite size corrections for neural network Gaussian processes Joseph M. Antognini	2019-08-27
Using Contextual Information to Improve Blood Glucose Prediction Mohammad Akbari • Rumi Chunara	2019-08-24
Adaptive Configuration Oracle for Online Portfolio Selection Methods Favour M. Nyikosa • Michael A. Osborne • Stephen J. Roberts	2019-08-22
Are Registration Uncertainty and Error Monotonically Associated Jie Luo • Sarah Frisken • Duo Wang • Alexandra Golby • Masashi Sugiyama • William M. Wells III	2019-08-21
Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy Collin A. Politsch • Jessi Cisewski-Kehe • Rupert A. C. Croft • Larry Wasserman	2019-08-20
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models Guoli Song • Shuhui Wang • Qingming Huang • Qi Tian	2019-08-14
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension Ye Xue • Diego Klabjan • Yuan Luo	2019-08-12
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold YoungJoon Yoo • Sangdoo Yun • Hyung Jin Chang • Yiannis Demiris • Jin Young Choi	2019-08-12
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations \| Ksenia Korovina • Sailun Xu • Kirthevasan Kandasamy • Willie Neiswanger • Barnabas Poczos • Jeff Schneider • Eric P. Xing	2019-08-05
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology Chundra Cathcart	2019-08-01
Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression Sevda Shabani • Saeed Samadianfard • Mohammad Taghi Sattari • Shahab Shamshirband • Amir Mosavi • Tibor Kmet • Annamaria R. Varkonyi-Koczy	2019-08-01
Scalable Bayesian Non-linear Matrix Completion Xiangju Qin • Paul Blomstedt • Samuel Kaski	2019-07-31
Sequential Learning of Active Subspaces Nathan Wycoff • Mickael Binois • Stefan M. Wild	2019-07-26
Bayesian Volumetric Autoregressive generative models for better semisupervised learning \| Guilherme Pombo • Robert Gray • Tom Varsavsky • John Ashburner • Parashkev Nachev	2019-07-26
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Sayan Ghosh • Jesper Kristensen • Yiming Zhang • Waad Subber • Liping Wang	2019-07-26
Towards Scalable Gaussian Process Modeling Piyush Pandita • Jesper Kristensen • Liping Wang	2019-07-25
Accelerating Experimental Design by Incorporating Experimenter Hunches Cheng Li • Santu Rana • Sunil Gupta • Vu Nguyen • Svetha Venkatesh • Alessandra Sutti • David Rubin • Teo Slezak • Murray Height • Mazher Mohammed • Ian Gibson	2019-07-22
A Multiple Continuous Signal Alignment Algorithm with Gaussian Process Profiles and an Application to Paleoceanography Taehee Lee • Lorraine E. Lisiecki • Devin Rand • Geoffrey Gebbie • Charles E. Lawrence	2019-07-20
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs Yusuke Tanaka • Toshiyuki Tanaka • Tomoharu Iwata • Takeshi Kurashima • Maya Okawa • Yasunori Akagi • Hiroyuki Toda	2019-07-19
Kernel Mode Decomposition and programmable/interpretable regression networks \| Houman Owhadi • Clint Scovel • Gene Ryan Yoo	2019-07-19
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach Abdallah A. Chehade • Ala A. Hussein	2019-07-19
Structured Variational Inference in Unstable Gaussian Process State Space Models \| Silvan Melchior • Sebastian Curi • Felix Berkenkamp • Andreas Krause	2019-07-16
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach Bin Liu • Yu Qi • Ke-Jia Chen	2019-07-15
The Use of Gaussian Processes in System Identification Simo Särkkä	2019-07-13
Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification Felix Batsch • Alireza Daneshkhah • Madeline Cheah • Stratis Kanarachos • Anthony Baxendale	2019-07-11
Gaussian Processes for Analyzing Positioned Trajectories in Sports Yuxin Zhao • Feng Yin • Fredrik Gunnarsson • Fredrik Hultkrantz	2019-07-05
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design Akshay Iyer • Yichi Zhang • Aditya Prasad • Siyu Tao • Yixing Wang • Linda Schadler • L Catherine Brinson • Wei Chen	2019-07-04
Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels Daniel Augusto R. M. A. de Souza • César Lincoln C. Mattos • João Paulo P. Gomes	2019-07-03
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches Yuqing Zhang • Neil Walton	2019-07-02
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time \| Christoph Heindl • Thomas Pönitz • Gernot Stübl • Andreas Pichler • Josef Scharinger	2019-07-01
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning Edwin Simpson • Erik-L{\^a}n Do Dinh • Tristan Miller • Iryna Gurevych	2019-07-01
Multi-objective multi-generation Gaussian process optimizer for design optimization Xiaobiao Huang • Minghao Song • Zhe Zhang	2019-06-29
Learning Fair Representations for Kernel Models \| Zilong Tan • Samuel Yeom • Matt Fredrikson • Ameet Talwalkar	2019-06-27
Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics \| Sebastian Riedel • Freek Stulp	2019-06-27
Modulating Surrogates for Bayesian Optimization Erik Bodin • Markus Kaiser • Ieva Kazlauskaite • Zhenwen Dai • Neill D. F. Campbell • Carl Henrik Ek	2019-06-26
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field \| Yaohui Guo • Vinay Varma Kalidindi • Mansur Arief • Wenshuo Wang • Jiacheng Zhu • Huei Peng • Ding Zhao	2019-06-25
Compositionally-Warped Gaussian Processes Gonzalo Rios • Felipe Tobar	2019-06-23
Sparse Spectrum Gaussian Process for Bayesian Optimization Ang Yang • Cheng Li • Santu Rana • Sunil Gupta • Svetha Venkatesh	2019-06-21
Tomographic Reconstruction of Triaxial Strain Fields from Bragg-Edge Neutron Imaging \| J. N. Hendriks • A. W. T. Gregg • R. R. Jackson • C. M. Wensrich • A. Wills • A. S. Tremsin • T. Shinohara • V. Luzin • O. Kirstein	2019-06-20
Bayesian Optimisation over Multiple Continuous and Categorical Inputs \| Binxin Ru • Ahsan S. Alvi • Vu Nguyen • Michael A. Osborne • Stephen J Roberts	2019-06-20
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances \| Csaba Toth • Harald Oberhauser	2019-06-19
Bayesian inverse regression for dimension reduction with small datasets Xin Cai • Guang Lin • Jinglai Li	2019-06-19
Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck • Theodoros Damoulas • Kangrui Wang • Mark Girolami	2019-06-19
Bayesian Optimization with Binary Auxiliary Information Yehong Zhang • Zhongxiang Dai • Kian Hsiang Low	2019-06-17
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models \| Alessandro Davide Ialongo • Mark van der Wilk • James Hensman • Carl Edward Rasmussen	2019-06-13
Robust Regression for Safe Exploration in Control Anqi Liu • Guanya Shi • Soon-Jo Chung • Anima Anandkumar • Yisong Yue	2019-06-13
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective Omry Cohen • Or Malka • Zohar Ringel	2019-06-12
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics Jacobo Roa-Vicens • Cyrine Chtourou • Angelos Filos • Francisco Rullan • Yarin Gal • Ricardo Silva	2019-06-11
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories Guangyi Zhang • Reza Ashrafi • Anne Juuti • Kirsi Pietiläinen • Pekka Marttinen	2019-06-10
Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study \| Chao Zhai • Hung D. Nguyen	2019-06-09
A Variant of Gaussian Process Dynamical Systems Jing Zhao • Jingjing Fei • Shiliang Sun	2019-06-09
Structured Variational Inference in Continuous Cox Process Models \| Virginia Aglietti • Edwin V. Bonilla • Theodoros Damoulas • Sally Cripps	2019-06-07
A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM Linning Xu • Feng Yin • Jiawei Zhang • Zhi-Quan Luo • Shuguang Cui	2019-06-06
Bayesian Optimization of Composite Functions \| Raul Astudillo • Peter I. Frazier	2019-06-04
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control Armin Lederer • Jonas Umlauft • Sandra Hirche	2019-06-04
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement Ming Lin • Xiaomin Song • Qi Qian • Hao Li • Liang Sun • Shenghuo Zhu • Rong Jin	2019-06-03
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process Sahar Zafari • Mariia Murashkina • Tuomas Eerola • Jouni Sampo • Heikki Kälviäinen • Heikki Haario	2019-06-03
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel \| Xin Qiu • Elliot Meyerson • Risto Miikkulainen	2019-06-03
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes Li-Fang Cheng • Bianca Dumitrascu • Michael Zhang • Corey Chivers • Michael Draugelis • Kai Li • Barbara E. Engelhardt	2019-06-01
Neural Likelihoods for Multi-Output Gaussian Processes Martin Jankowiak • Jacob Gardner	2019-05-31
Enriched Mixtures of Gaussian Process Experts Charles W. L. Gadd • Sara Wade • Alexis Boukouvalas	2019-05-30
Efficient EM-Variational Inference for Hawkes Process Feng Zhou • Zhidong Li • Xuhui Fan • Yang Wang • Arcot Sowmya • Fang Chen	2019-05-29
Adversarial Robustness Guarantees for Classification with Gaussian Processes Arno Blaas • Andrea Patane • Luca Laurenti • Luca Cardelli • Marta Kwiatkowska • Stephen Roberts	2019-05-28
Recursive Estimation for Sparse Gaussian Process Regression \| Manuel Schürch • Dario Azzimonti • Alessio Benavoli • Marco Zaffalon	2019-05-28
Scalable Training of Inference Networks for Gaussian-Process Models \| Jiaxin Shi • Mohammad Emtiyaz Khan • Jun Zhu	2019-05-27
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation \| Théo Galy-Fajou • Florian Wenzel • Christian Donner • Manfred Opper	2019-05-23
Learning spectrograms with convolutional spectral kernels Zheyang Shen • Markus Heinonen • Samuel Kaski	2019-05-23
A Bulirsch-Stoer algorithm using Gaussian processes Philip G. Breen • Christopher N. Foley	2019-05-23
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation Joseph Guinness	2019-05-20
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization Jungtaek Kim • Seungjin Choi	2019-05-18
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations Timur Takhtaganov • Zarija Lukic • Juliane Mueller • Dmitriy Morozov	2019-05-17
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal • Jonathan H. Huggins • Brian Trippe • Tamara Broderick	2019-05-16
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions \| Tim Pearce • Russell Tsuchida • Mohamed Zaki • Alexandra Brintrup • Andy Neely	2019-05-15
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes Chengyao Sun • Weisi Guo	2019-05-15
Online Anomaly Detection with Sparse Gaussian Processes Jingjing Fei • Shiliang Sun	2019-05-14
Adaptive surrogate models for parametric studies Jan N. Fuhg	2019-05-12
Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization Kanrun Huang • Tucker Hermans	2019-05-10
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models \| Francisco Sahli Costabal • Paris Perdikaris • Ellen Kuhl • Daniel E. Hurtado	2019-05-09
Knowing The What But Not The Where in Bayesian Optimization Vu Nguyen • Michael A. Osborne	2019-05-07
A deep learning approach for analyzing the composition of chemometric data Muhammad Bilal • Mohib Ullah	2019-05-07
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations \| Marko Järvenpää • Michael Gutmann • Aki Vehtari • Pekka Marttinen	2019-05-03
Modular Deep Probabilistic Programming \| Zhenwen Dai • Eric Meissner • Neil D. Lawrence	2019-05-01
A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics Alberto Dalla Libera • Ruggero Carli	2019-04-30
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms Kaifeng Yang • Michael Emmerich • André Deutz • Thomas Bäck	2019-04-26
A Bayesian Approach for the Robust Optimisation of Expensive-To-Evaluate Functions Nicholas D. Sanders • Richard M. Everson • Jonathan E. Fieldsend • Alma A. M. Rahat	2019-04-25
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees Jeremiah Zhe Liu	2019-04-21
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes Ognjen Rudovic • Yuria Utsumi • Ricardo Guerrero • Kelly Peterson • Daniel Rueckert • Rosalind W. Picard	2019-04-19
A Bayesian Perspective on the Deep Image Prior \| Zezhou Cheng • Matheus Gadelha • Subhransu Maji • Daniel Sheldon	2019-04-16
Multi-View Stereo by Temporal Nonparametric Fusion \| Yuxin Hou • Juho Kannala • Arno Solin	2019-04-12
Scalarizing Functions in Bayesian Multiobjective Optimization Tinkle Chugh	2019-04-11
Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms Md. Maniruzzaman • Md. Jahanur Rahman • Benojir Ahammed • Md. Menhazul Abedin • Harman S. Suri • Mainak Biswas • Ayman El-Baz • Petros Bangeas • Georgios Tsoulfas • Jasjit S. Suri	2019-04-08
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources Edwin Simpson • Steven Reece • Stephen J. Roberts	2019-04-05
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification Andrew Patterson • Arun Lakshmanan • Naira Hovakimyan	2019-04-04
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events Jonathan Vandermause • Steven B. Torrisi • Simon Batzner • Alexie M. Kolpak • Boris Kozinsky	2019-04-03
Sentiment analysis with genetically evolved Gaussian kernels Ibai Roman • Alexander Mendiburu • Roberto Santana • Jose A. Lozano	2019-04-01
MCTS-based Automated Negotiation Agent (Extended Abstract) Cédric Buron • Zahia Guessoum • Sylvain Ductor	2019-03-29
A Gaussian process latent force model for joint input-state estimation in linear structural systems Rajdip Nayek • Souvik Chakraborty • Sriram Narasimhan	2019-03-29
Using Gaussian process regression for efficient parameter reconstruction Philipp-Immanuel Schneider • Martin Hammerschmidt • Lin Zschiedrich • Sven Burger	2019-03-28
Pixelation is NOT Done in Videos Yet Jizhe Zhou • Chi-Man Pun • YingYu Wang	2019-03-26
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar • Ilias Bilionis • Xiaoqi Liu • Panagiota Karava • Athanasios Tzempelikos	2019-03-21
Deep Gaussian Processes for Multi-fidelity Modeling Kurt Cutajar • Mark Pullin • Andreas Damianou • Neil Lawrence • Javier González	2019-03-18
Financial Applications of Gaussian Processes and Bayesian Optimization Joan Gonzalvez • Edmond Lezmi • Thierry Roncalli • Jiali Xu	2019-03-12
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations Astrid Dahl • Edwin V. Bonilla	2019-03-10
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data Seokhyun Chung • Raed Kontar	2019-03-09
Rates of Convergence for Sparse Variational Gaussian Process Regression \| David R. Burt • Carl E. Rasmussen • Mark van der Wilk	2019-03-08
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation Jiameng Fan • Wenchao Li	2019-03-06
A Bayesian Approach to Triaxial Strain Tomography from High-energy X-ray Diffraction J. N. Hendriks • C. M. Wensrich • A. Wills	2019-03-06
Deep Learning and Gaussian Process based Band Assignment in Dual Band Systems Daoud Burghal • Rui Wang • Andreas F. Molisch	2019-02-28
Estimating Local Function Complexity via Mixture of Gaussian Processes Danny Panknin • Shinichi Nakajima • Thanh Binh Bui • Klaus-Robert Müller	2019-02-27
Stable Bayesian Optimisation via Direct Stability Quantification Alistair Shilton • Sunil Gupta • Santu Rana • Svetha Venkatesh • Majid Abdolshah • Dang Nguyen	2019-02-21
Bayesian optimisation under uncertain inputs Rafael Oliveira • Lionel Ott • Fabio Ramos	2019-02-21
Gaussian Process Priors for Dynamic Paired Comparison Modelling \| Martin Ingram	2019-02-20
Multifidelity Bayesian Optimization for Binomial Output Leonid Matyushin • Alexey Zaytsev • Oleg Alenkin • Andrey Ustuzhanin	2019-02-19
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee • Lechao Xiao • Samuel S. Schoenholz • Yasaman Bahri • Roman Novak • Jascha Sohl-Dickstein • Jeffrey Pennington	2019-02-18
The Kalai-Smorodinski solution for many-objective Bayesian optimization Mickaël Binois • Victor Picheny • Patrick Taillandier • Abderrahmane Habbal	2019-02-18
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification Yue Xu • Feng Yin • Wenjun Xu • Jiaru Lin • Shuguang Cui	2019-02-13
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang	2019-02-13
Learning interpretable continuous-time models of latent stochastic dynamical systems Lea Duncker • Gergo Bohner • Julien Boussard • Maneesh Sahani	2019-02-12
Multi-objective Bayesian optimisation with preferences over objectives Majid Abdolshah • Alistair Shilton • Santu Rana • Sunil Gupta • Svetha Venkatesh	2019-02-12
Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization Bin Liu	2019-02-11
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping \| Michael Moor • Max Horn • Bastian Rieck • Damian Roqueiro • Karsten Borgwardt	2019-02-05
Combinatorial Bayesian Optimization using the Graph Cartesian Product Changyong Oh • Jakub M. Tomczak • Efstratios Gavves • Max Welling	2019-02-01
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach Raed Kontar • Garvesh Raskutti • Shiyu Zhou	2019-01-31
End-to-End Probabilistic Inference for Nonstationary Audio Analysis \| William J. Wilkinson • Michael Riis Andersen • Joshua D. Reiss • Dan Stowell • Arno Solin	2019-01-31
Geometric fluid approximation for general continuous-time Markov chains Michalis Michaelides • Jane Hillston • Guido Sanguinetti	2019-01-31
Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias • Jonathan Schwarz • Alexander G. de G. Matthews • Razvan Pascanu • Yee Whye Teh	2019-01-31
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis Kohei Hayashi • Masaaki Imaizumi • Yuichi Yoshida	2019-01-28
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin • Heiko Strathmann • Gunnar Rätsch	2019-01-23
Active Learning with Gaussian Processes for High Throughput Phenotyping \| Sumit Kumar • Wenhao Luo • George Kantor • Katia Sycara	2019-01-21
Modeling and inference of spatio-temporal protein dynamics across brain networks Sara Garbarino • Marco Lorenzi	2019-01-18
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models Shiwei Lan	2019-01-13
Gaussian processes with linear operator inequality constraints Christian Agrell	2019-01-10
No-Regret Bayesian Optimization with Unknown Hyperparameters Felix Berkenkamp • Angela P. Schoellig • Andreas Krause	2019-01-10
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models Randy Ardywibowo • Guang Zhao • Zhangyang Wang • Bobak Mortazavi • Shuai Huang • Xiaoning Qian	2019-01-08
Variational bridge constructs for approximate Gaussian process regression Wil O C Ward • Mauricio A Álvarez	2019-01-07
Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts Andrea Marziali • Emanuele Fabbiani • Giuseppe De Nicolao	2019-01-04
Approximate Inference for Multiplicative Latent Force Models Daniel J. Tait • Bruce J. Worton	2018-12-31
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language \| Jamie Fairbrother • Christopher Nemeth • Maxime Rischard • Johanni Brea • Thomas Pinder	2018-12-21
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation \| Xiaoliang Dai • Peizhao Zhang • Bichen Wu • Hongxu Yin • Fei Sun • Yanghan Wang • Marat Dukhan • Yunqing Hu • Yiming Wu • Yangqing Jia • Peter Vajda • Matt Uyttendaele • Niraj K. Jha	2018-12-21
Towards an Evolvable Cancer Treatment Simulator Richard J. Preen • Larry Bull • Andrew Adamatzky	2018-12-19
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion Seungjoon Lee • Felix Dietrich • George E. Karniadakis • Ioannis G. Kevrekidis	2018-12-16
Efficient Model-Free Reinforcement Learning Using Gaussian Process \| Ying Fan • Letian Chen • Yizhou Wang	2018-12-11
Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest Keir K. Rogers • Hiranya V. Peiris • Andrew Pontzen • Simeon Bird • Licia Verde • Andreu Font-Ribera	2018-12-11
The Limitations of Model Uncertainty in Adversarial Settings Kathrin Grosse • David Pfaff • Michael Thomas Smith • Michael Backes	2018-12-06
Safe Active Learning for Time-Series Modeling with Gaussian Processes Christoph Zimmer • Mona Meister • Duy Nguyen-Tuong	2018-12-01
Learning a latent manifold of odor representations from neural responses in piriform cortex Anqi Wu • Stan Pashkovski • Sandeep R. Datta • Jonathan W. Pillow	2018-12-01
A Bayes-Sard Cubature Method Toni Karvonen • Chris J. Oates • Simo Sarkka	2018-12-01
Scalable Hyperparameter Transfer Learning Valerio Perrone • Rodolphe Jenatton • Matthias W. Seeger • Cedric Archambeau	2018-12-01
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments Mahdi Imani • Seyede Fatemeh Ghoreishi • Ulisses M. Braga-Neto	2018-12-01
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes Rico Angell • Daniel R. Sheldon	2018-12-01
Temporal alignment and latent Gaussian process factor inference in population spike trains Lea Duncker • Maneesh Sahani	2018-12-01
Deep Factors with Gaussian Processes for Forecasting Danielle C. Maddix • Yuyang Wang • Alex Smola	2018-11-30
Neural Non-Stationary Spectral Kernel \| Sami Remes • Markus Heinonen • Samuel Kaski	2018-11-27
Partitioned Variational Inference: A unified framework encompassing federated and continual learning Thang D. Bui • Cuong V. Nguyen • Siddharth Swaroop • Richard E. Turner	2018-11-27
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence Xiu Yang • David Barajas-Solano • Guzel Tartakovsky • Alexandre Tartakovsky	2018-11-24
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior \| Zi Wang • Beomjoon Kim • Leslie Pack Kaelbling	2018-11-23
Mixed Likelihood Gaussian Process Latent Variable Model Samuel Murray • Hedvig Kjellström	2018-11-19
Recursive Sparse Pseudo-input Gaussian Process SARSA John Martin • Brendan Englot	2018-11-17
Mean Square Prediction Error of Misspecified Gaussian Process Models Thomas Beckers • Jonas Umlauft • Sandra Hirche	2018-11-16
Reachability-based safe learning for optimal control problem Stanislav Fedorov • Antonio Candelieri	2018-11-09
Unifying Probabilistic Models for Time-Frequency Analysis \| William J. Wilkinson • Michael Riis Andersen • Joshua D. Reiss • Dan Stowell • Arno Solin	2018-11-06
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu • Yew-Soon Ong • Jianfei Cai	2018-11-03
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu • Jianfei Cai • Yew-Soon Ong • Yi Wang	2018-11-03
Multiplicative Latent Force Models Daniel J. Tait • Bruce J. Worton	2018-11-01
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models Ulrich Paquet • Marco Fraccaro	2018-10-28
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands • Daniel B. Neill • Hannes Nickisch • Andrew Gordon Wilson	2018-10-28
Gaussian Process Prior Variational Autoencoders \| Francesco Paolo Casale • Adrian V Dalca • Luca Saglietti • Jennifer Listgarten • Nicolo Fusi	2018-10-28
Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic • Jonathan Scarlett • Stefanie Jegelka • Volkan Cevher	2018-10-25
Data Association with Gaussian Processes Markus Kaiser • Clemens Otte • Thomas Runkler • Carl Henrik Ek	2018-10-16
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models Kaspar Märtens • Kieran R. Campbell • Christopher Yau	2018-10-16
Hyperparameter Learning via Distributional Transfer \| Ho Chung Leon Law • Peilin Zhao • Lucian Chan • Junzhou Huang • Dino Sejdinovic	2018-10-15
Non-linear process convolutions for multi-output Gaussian processes Mauricio A. Álvarez • Wil O. C. Ward • Cristian Guarnizo	2018-10-10
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Filip Tronarp • Hans Kersting • Simo Särkkä • Philipp Hennig	2018-10-08
Deep convolutional Gaussian processes \| Kenneth Blomqvist • Samuel Kaski • Markus Heinonen	2018-10-06
GPdoemd: a Python package for design of experiments for model discrimination \| Simon Olofsson • Lukas Hebing • Sebastian Niedenführ • Marc Peter Deisenroth • Ruth Misener	2018-10-05
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks Juho Lee • Yoonho Lee • Jungtaek Kim • Adam R. Kosiorek • Seungjin Choi • Yee Whye Teh	2018-10-01
Adaptive Gaussian process surrogates for Bayesian inference Timur Takhtaganov • Juliane Müller	2018-09-27
High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants Vijay Varma • Davide Gerosa • François Hébert • Leo C. Stein • Hao Zhang	2018-09-24
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models \| Shoubo Hu • Zhitang Chen • Vahid Partovi Nia • Laiwan Chan • Yanhui Geng	2018-09-23
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities Yusuke Tanaka • Tomoharu Iwata • Toshiyuki Tanaka • Takeshi Kurashima • Maya Okawa • Hiroyuki Toda	2018-09-21
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics \| Young-Jin Park • Han-Lim Choi	2018-09-19
A Generalized Representer Theorem for Hilbert Space - Valued Functions Sanket Diwale • Colin Jones	2018-09-19
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze Diego Romeres • Devesh Jha • Alberto Dalla Libera • William Yerazunis • Daniel Nikovski	2018-09-13
Gaussian process classification using posterior linearisation Ángel F. García-Fernández • Filip Tronarp • Simo Särkkä	2018-09-13
Gaussian Process Classification for Variable Fidelity Data \| Nikita Klyuchnikov • Evgeny Burnaev	2018-09-13
Probabilistic approach to limited-data computed tomography reconstruction Zenith Purisha • Carl Jidling • Niklas Wahlström • Simo Särkkä • Thomas B. Schön	2018-09-11
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence Xiu Yang • Guzel Tartakovsky • Alexandre Tartakovsky	2018-09-10
Gaussian Process Regression for Binned Data Michael Thomas Smith • Mauricio A Alvarez • Neil D Lawrence	2018-09-06
Bayesian Nonparametric Spectral Estimation \| Felipe Tobar	2018-09-06
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease Muriel Lang • Franz M. J. Pfister • Jakob Fröhner • Kian Abedinpour • Daniel Pichler • Urban Fietzek • Terry T. Um • Dana Kulić • Satoshi Endo • Sandra Hirche	2018-08-31
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female \| Dewan Fayzur	2018-08-26
Continuous time Gaussian process dynamical models in gene regulatory network inference Atte Aalto • Lauri Viitasaari • Pauliina Ilmonen • Laurent Mombaerts • Jorge Goncalves	2018-08-24
Deep Convolutional Networks as shallow Gaussian Processes \| Adrià Garriga-Alonso • Carl Edward Rasmussen • Laurence Aitchison	2018-08-16
Locally-adaptive Bayesian nonparametric inference for phylodynamics James R. Faulkner • Andrew F. Magee • Beth Shapiro • Vladimir N. Minin	2018-08-13
Exploiting Structure for Fast Kernel Learning \| Trefor W. Evans • Prasanth B. Nair	2018-08-09
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting Stephanie Tan • Hayley Hung	2018-08-06
Machine Learning of Space-Fractional Differential Equations Mamikon Gulian • Maziar Raissi • Paris Perdikaris • George Karniadakis	2018-08-02
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner • Manfred Opper	2018-08-02
A Data-Efficient Approach to Precise and Controlled Pushing Maria Bauza • Francois R. Hogan • Alberto Rodriguez	2018-07-26
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization Zhaozhong Chen • Christoffer Heckman • Simon Julier • Nisar Ahmed	2018-07-23
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda • Katherine Driggs-Campbell • Mykel J. Kochenderfer	2018-07-22
Deep Learning for Epidemiological Predictions Wu • Yuexin Yang • Yiming Nishiura • Hiroshi Saitoh • Masaya	2018-07-21
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation Xiong Lyu • Mickael Binois • Michael Ludkovski	2018-07-18
Battery health prediction under generalized conditions using a Gaussian process transition model Robert R. Richardson • Michael A. Osborne • David A. Howey	2018-07-17
Machine Learning of Energetic Material Properties Brian C. Barnes • Daniel C. Elton • Zois Boukouvalas • DeCarlos E. Taylor • William D. Mattson • Mark D. Fuge • Peter W. Chung	2018-07-17
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors Danil Kuzin • Olga Isupova • Lyudmila Mihaylova	2018-07-15
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures Andrew R. Lawrence • Carl Henrik Ek • Neill D. F. Campbell	2018-07-12
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion \| Steven Atkinson • Nicholas Zabaras	2018-07-11
An Empirical Approach For Probing the Definiteness of Kernels Martin Zaefferer • Thomas Bartz-Beielstein • Günter Rudolph	2018-07-10
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning Danil Kuzin • Le Yang • Olga Isupova • Lyudmila Mihaylova	2018-07-09
A Tutorial on Bayesian Optimization \| Peter I. Frazier	2018-07-08
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences Motonobu Kanagawa • Philipp Hennig • Dino Sejdinovic • Bharath K Sriperumbudur	2018-07-06
Neural Processes \| Marta Garnelo • Jonathan Schwarz • Dan Rosenbaum • Fabio Viola • Danilo J. Rezende • S. M. Ali Eslami • Yee Whye Teh	2018-07-04
Empirical fixed point bifurcation analysis \| Gergo Bohner • Maneesh Sahani	2018-07-04
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu • Yew-Soon Ong • Xiaobo Shen • Jianfei Cai	2018-07-03
Variational Network Inference: Strong and Stable with Concrete Support Amir Dezfouli • Edwin Bonilla • Richard Nock	2018-07-01
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data Minyoung Kim	2018-07-01
Structured Variationally Auto-encoded Optimization Xiaoyu Lu • Javier Gonzalez • Zhenwen Dai • Neil Lawrence	2018-07-01
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning Ahmed Alaa • Mihaela Schaar	2018-07-01
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) Trefor Evans • Prasanth Nair	2018-07-01
Bayesian Deep Learning on a Quantum Computer \| Zhikuan Zhao • Alejandro Pozas-Kerstjens • Patrick Rebentrost • Peter Wittek	2018-06-29
Context-Aware Pedestrian Motion Prediction In Urban Intersections Golnaz Habibi • Nikita Jaipuria • Jonathan P. How	2018-06-25
Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization Young-Jin Park • Piyush M. Tagade • Han-Lim Choi	2018-06-21
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems \| Thomas Beckers • Dana Kulić • Sandra Hirche	2018-06-19
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors Yichi Zhang • Siyu Tao • Wei Chen • Daniel W. Apley	2018-06-19
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms Samuel Wiqvist • Umberto Picchini • Julie Lyng Forman	2018-06-15
Grouped Gaussian Processes for Solar Power Prediction Astrid Dahl • Edwin V. Bonilla	2018-06-07
Finding Convincing Arguments Using Scalable Bayesian Preference Learning Edwin Simpson • Iryna Gurevych	2018-06-06
Killing Four Birds with one Gaussian Process: Analyzing Test-Time Attack Vectors on Classification Kathrin Grosse • Michael T. Smith • Michael Backes	2018-06-06
Pathwise Derivatives Beyond the Reparameterization Trick Martin Jankowiak • Fritz Obermeyer	2018-06-05
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes Seyed Mostafa Kia • Andre Marquand	2018-06-04
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression \| Haitao Liu • Jianfei Cai • Yi Wang • Yew-Soon Ong	2018-06-03
A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos Chung-Ching Lin • Ying Hung	2018-06-01
Multi-Task Learning by Maximizing Statistical Dependence Youssef A. Mejjati • Darren Cosker • Kwang In Kim	2018-06-01
Tight Regret Bounds for Bayesian Optimization in One Dimension Jonathan Scarlett	2018-05-30
Efficient Bayesian Inference for a Gaussian Process Density Model Christian Donner • Manfred Opper	2018-05-29
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification Dimitrios Milios • Raffaello Camoriano • Pietro Michiardi • Lorenzo Rosasco • Maurizio Filippone	2018-05-28
Bias correction in daily maximum and minimum temperature measurements through Gaussian process modeling Maxime Rischard • Natesh Pillai • Karen A. McKinnon	2018-05-25
Efficient Inference in Multi-task Cox Process Models \| Virginia Aglietti • Theodoros Damoulas • Edwin Bonilla	2018-05-24
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems Trong Nghia Hoang • Quang Minh Hoang • Kian Hsiang Low • Jonathan How	2018-05-23
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models Anton Mallasto • Søren Hauberg • Aasa Feragen	2018-05-23
Structured Bayesian Gaussian process latent variable model Steven Atkinson • Nicholas Zabaras	2018-05-22
Online Learning in Kernelized Markov Decision Processes Sayak Ray Chowdhury • Aditya Gopalan	2018-05-21
Accelerated Bayesian Optimization throughWeight-Prior Tuning Alistair Shilton • Sunil Gupta • Santu Rana • Pratibha Vellanki • Laurence Park • Cheng Li • Svetha Venkatesh • Alessandra Sutti • David Rubin • Thomas Dorin • Alireza Vahid • Murray Height • Teo Slezak	2018-05-21
Heterogeneous Multi-output Gaussian Process Prediction \| Pablo Moreno-Muñoz • Antonio Artés-Rodríguez • Mauricio A. Álvarez	2018-05-19
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes Cristian Guarnizo • Mauricio A. Álvarez	2018-05-18
Spatial Uncertainty Sampling for End-to-End Control Alexander Amini • Ava Soleimany • Sertac Karaman • Daniela Rus	2018-05-13
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization Juan Cruz Barsce • Jorge A. Palombarini • Ernesto C. Martínez	2018-05-12
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes Eduardo C. Garrido-Merchán • Daniel Hernández-Lobato	2018-05-09
Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model Oscar Claveria • Enric Monte • Salvador Torra	2018-05-02
Gaussian Process Behaviour in Wide Deep Neural Networks \| Alexander G. de G. Matthews • Mark Rowland • Jiri Hron • Richard E. Turner • Zoubin Ghahramani	2018-04-30
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact Sahil Garg	2018-04-27
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots Sahil Garg • Nora Ayanian	2018-04-27
Learning Manifolds from Non-stationary Streaming Data Suchismit Mahapatra • Varun Chandola	2018-04-24
Gaussian Material Synthesis Károly Zsolnai-Fehér • Peter Wonka • Michael Wimmer	2018-04-23
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems Mustafa A. Mohamad • Themistoklis P. Sapsis	2018-04-19
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs \| Philippe Wenk • Alkis Gotovos • Stefan Bauer • Nico Gorbach • Andreas Krause • Joachim M. Buhmann	2018-04-12
Maximum likelihood estimation for Gaussian processes under inequality constraints François Bachoc • Agnès Lagnoux • Andrés F. López-Lopera	2018-04-10
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps Manon Kok • Arno Solin	2018-04-05
Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality Benjamin Gutierrez Becker • Tassilo Klein • Christian Wachinger	2018-04-04
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models Charles Gadd • Sara Wade • Akeel Shah • Dimitris Grammatopoulos	2018-03-28
Distributed Adaptive Sampling for Kernel Matrix Approximation Daniele Calandriello • Alessandro Lazaric • Michal Valko	2018-03-27
Learning-based Model Predictive Control for Safe Exploration \| Torsten Koller • Felix Berkenkamp • Matteo Turchetta • Andreas Krause	2018-03-22
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation Jie Luo • Matt Toews • Ines Machado • Sarah Frisken • Miaomiao Zhang • Frank Preiswerk • Alireza Sedghi • Hongyi Ding • Steve Pieper • Polina Golland • Alexandra Golby • Masashi Sugiyama • William M. Wells III	2018-03-20
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents Mohamad Baydoun • Mahdyar Ravanbakhsh • Damian Campo • Pablo Marin • David Martin • Lucio Marcenaro • Andrea Cavallaro • Carlo S. Regazzoni	2018-03-17
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss • Jacob R. Gardner • Kilian Q. Weinberger • Andrew Gordon Wilson	2018-03-16
Gaussian Processes Over Graphs Arun Venkitaraman • Saikat Chatterjee • Peter Händel	2018-03-15
Capturing Structure Implicitly from Time-Series having Limited Data \| Daniel Emaasit • Matthew Johnson	2018-03-15
Variational zero-inflated Gaussian processes with sparse kernels \| Pashupati Hegde • Markus Heinonen • Samuel Kaski	2018-03-13
Active model learning and diverse action sampling for task and motion planning Zi Wang • Caelan Reed Garrett • Leslie Pack Kaelbling • Tomás Lozano-Pérez	2018-03-02
Conditionally Independent Multiresolution Gaussian Processes \| Jalil Taghia • Thomas B. Schön	2018-02-25
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13) \| Yuria Utsumi • Ognjen Rudovic • Kelly Peterson • Ricardo Guerrero • Rosalind W. Picard	2018-02-22
Learning Causally-Generated Stationary Time Series Wessel Bruinsma • Richard E. Turner	2018-02-22
Emulating dynamic non-linear simulators using Gaussian processes Hossein Mohammadi • Peter Challenor • Marc Goodfellow	2018-02-21
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning Ahmed M. Alaa • Mihaela van der Schaar	2018-02-20
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches Simon Olofsson • Marc Peter Deisenroth • Ruth Misener	2018-02-12
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer Ryosuke Kamesawa • Issei Sato • Masashi Sugiyama	2018-02-12
Gaussian Process Landmarking on Manifolds Tingran Gao • Shahar Z. Kovalsky • Ingrid Daubechies	2018-02-09
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models Michael Ying Yang • Wentong Liao • Yanpeng Cao • Bodo Rosenhahn	2018-02-09
Scalable Meta-Learning for Bayesian Optimization Matthias Feurer • Benjamin Letham • Eytan Bakshy	2018-02-06
Building machine learning force fields for nanoclusters Claudio Zeni • Kevin Rossi • Aldo Glielmo • Ádám Fekete • Nicola Gaston • Francesca Baletto • Alessandro De Vita	2018-02-05
Deep Learning based Retinal OCT Segmentation Mike Pekala • Neil Joshi • David E. Freund • Neil M. Bressler • Delia Cabrera DeBuc • Philippe M Burlina	2018-01-29
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann	2018-01-28
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians Akshay Rangesh • Mohan M. Trivedi	2018-01-24
Uncertainty quantification for computer models with spatial output using calibration-optimal bases James M Salter • Daniel B Williamson • John Scinocca • Viatcheslav Kharin	2018-01-24
Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering Ke Sun • Kelsey Saulnier • Nikolay Atanasov • George J. Pappas • Vijay Kumar	2018-01-23
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study \| Lorin Crawford • Seth R. Flaxman • Daniel E. Runcie • Mike West	2018-01-22
Upgrading from Gaussian Processes to Student's-T Processes Brendan D. Tracey • David H. Wolpert	2018-01-18
Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification Pushparaja Murugan	2018-01-03
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks Joseph Futoma • Anthony Lin • Mark Sendak • Armando Bedoya • Meredith Clement • Cara O'Brien • Katherine Heller	2018-01-01
Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks Shiliang Sun • Rongqing Huang • Ya Gao	2017-12-25
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data N. Olspert • J. Lehtinen • M. J. Käpylä • J. Pelt • A. Grigorievskiy	2017-12-21
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution \| Topi Paananen • Juho Piironen • Michael Riis Andersen • Aki Vehtari	2017-12-21
Safe Policy Search with Gaussian Process Models \| Kyriakos Polymenakos • Alessandro Abate • Stephen Roberts	2017-12-15
Practical Bayesian optimization in the presence of outliers Ruben Martinez-Cantin • Kevin Tee • Michael McCourt	2017-12-12
Shape optimization in laminar flow with a label-guided variational autoencoder Stephan Eismann • Stefan Bartzsch • Stefano Ermon	2017-12-10
Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82 Johanna Pasquet-Itam • Jérôme Pasquet	2017-12-07
Gaussian Process Regression for Arctic Coastal Erosion Forecasting Matthew Kupilik • Frank Witmer • Euan-Angus MacLeod • Caixia Wang • Tom Ravens	2017-12-04
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression \| Kelly Peterson • Ognjen Rudovic • Ricardo Guerrero • Rosalind W. Picard	2017-12-01
Novel Exploration Techniques (NETs) for Malaria Policy Interventions Oliver Bent • Sekou L. Remy • Stephen Roberts • Aisha Walcott-Bryant	2017-12-01
Variational Inference via \chi Upper Bound Minimization Adji Bousso Dieng • Dustin Tran • Rajesh Ranganath • John Paisley • David Blei	2017-12-01
Targeting EEG/LFP Synchrony with Neural Nets Yitong Li • Michael Murias • Samantha Major • Geraldine Dawson • Kafui Dzirasa • Lawrence Carin • David E. Carlson	2017-12-01
Cross-Spectral Factor Analysis Neil Gallagher • Kyle R. Ulrich • Austin Talbot • Kafui Dzirasa • Lawrence Carin • David E. Carlson	2017-12-01
Scalable synthesis of safety certificates from data with application to learning-based control Kim P. Wabersich • Melanie N. Zeilinger	2017-11-30
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes \| Iñigo Urteaga • David J. Albers • Marija Vlajic Wheeler • Anna Druet • Hans Raffauf • Noémie Elhadad	2017-11-30
A Neural Stochastic Volatility Model Rui Luo • Weinan Zhang • Xiaojun Xu • Jun Wang	2017-11-30
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation Christopher Tegho • Paweł Budzianowski • Milica Gašić	2017-11-30
Gaussian Process Neurons Learn Stochastic Activation Functions Sebastian Urban • Marcus Basalla • Patrick van der Smagt	2017-11-29
Variational Inference for Gaussian Process Models with Linear Complexity Ching-An Cheng • Byron Boots	2017-11-28
Dependent relevance determination for smooth and structured sparse regression Anqi Wu • Oluwasanmi Koyejo • Jonathan W. Pillow	2017-11-28
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models Jung-Su Ha • Hyeok-Joo Chae • Han-Lim Choi	2017-11-22
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs Trong Nghia Hoang • Quang Minh Hoang • Ruofei Ouyang • Kian Hsiang Low	2017-11-19
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization Shaunak D. Bopardikar • George S. Eskander Ekladious	2017-11-19
Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars Lukas Hewing • Alexander Liniger • Melanie N. Zeilinger	2017-11-17
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception Ruofei Ouyang • Kian Hsiang Low	2017-11-16
Joint Gaussian Processes for Biophysical Parameter Retrieval Daniel Heestermans Svendsen • Luca Martino • Manuel Campos-Taberner • Francisco Javier García-Haro • Gustau Camps-Valls	2017-11-14
Structured Variational Inference for Coupled Gaussian Processes Vincent Adam	2017-11-03
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation Roman Föll • Bernard Haasdonk • Markus Hanselmann • Holger Ulmer	2017-11-02
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression Haibin Yu • Trong Nghia Hoang • Kian Hsiang Low • Patrick Jaillet	2017-11-01
Locally stationary spatio-temporal interpolation of Argo profiling float data Mikael Kuusela • Michael L. Stein	2017-11-01
Learning Kernels over Strings using Gaussian Processes Daniel Beck • Trevor Cohn	2017-11-01
Auto-Differentiating Linear Algebra Matthias Seeger • Asmus Hetzel • Zhenwen Dai • Eric Meissner • Neil D. Lawrence	2017-10-24
Finite-dimensional Gaussian approximation with linear inequality constraints \| Andrés F. López-Lopera • François Bachoc • Nicolas Durrande • Olivier Roustant	2017-10-20
Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots Varuna De Silva • Jamie Roche • Ahmet Kondoz	2017-10-17
Deep Gaussian Covariance Network Kevin Cremanns • Dirk Roos	2017-10-17
Safe Learning of Quadrotor Dynamics Using Barrier Certificates Li Wang • Evangelos A. Theodorou • Magnus Egerstedt	2017-10-16
Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement Michael Ludkovski • James Risk	2017-10-14
Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser • Clemens Otte • Thomas Runkler • Carl Henrik Ek	2017-10-08
Machine Learning for Drug Overdose Surveillance Daniel B. Neill • William Herlands	2017-10-06
Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators Nathan Banka • W. Tony Piaskowy • Joseph Garbini • Santosh Devasia	2017-10-05
Remote Sensing Image Classification with Large Scale Gaussian Processes Pablo Morales-Alvarez • Adrian Perez-Suay • Rafael Molina • Gustau Camps-Valls	2017-10-02
Forecasting of commercial sales with large scale Gaussian Processes \| Rodrigo Rivera • Evgeny Burnaev	2017-09-16
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty Rafael Oliveira • Lionel Ott • Vitor Guizilini • Fabio Ramos	2017-09-07
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection Joseph Futoma • Sanjay Hariharan • Mark Sendak • Nathan Brajer • Meredith Clement • Armando Bedoya • Cara O'Brien • Katherine Heller	2017-08-19
Frequentist coverage and sup-norm convergence rate in Gaussian process regression Yun Yang • Anirban Bhattacharya • Debdeep Pati	2017-08-16
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain \| Dianbo Liu • Fengjiao Peng • Andrew Shea • Ognjen • Rudovic • Rosalind Picard	2017-08-09
SSN\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model Angel Deborah S • S Milton Rajendram • T T Mirnalinee	2017-08-01
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression Pengfei Wei • Ramon Sagarna • Yiping Ke • Yew-Soon Ong • Chi-Keong Goh	2017-08-01
Preferential Bayesian Optmization Javier González • Zhenwen Dai • Andreas Damianou • Neil D. Lawrence	2017-08-01
Distributed Batch Gaussian Process Optimization Erik A. Daxberger • Bryan Kian Hsiang Low	2017-08-01
High Dimensional Bayesian Optimization with Elastic Gaussian Process Santu Rana • Cheng Li • Sunil Gupta • Vu Nguyen • Svetha Venkatesh	2017-08-01
SSN\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier Angel Deborah S • S Milton Rajendram • T T Mirnalinee	2017-08-01
Sequential design of experiments to estimate a probability of exceeding a threshold in a multi-fidelity stochastic simulator Rémi Stroh • Séverine Demeyer • Nicolas Fischer • Julien Bect • Emmanuel Vazquez	2017-07-26
Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference Mustafa Mukadam • Jing Dong • Xinyan Yan • Frank Dellaert • Byron Boots	2017-07-24
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior Felipe Tobar • Gonzalo Rios • Tomás Valdivia • Pablo Guerrero	2017-07-19
Latent Gaussian Process Regression Erik Bodin • Neill D. F. Campbell • Carl Henrik Ek	2017-07-18
Enhanced Particle Swarm Optimization Algorithms for Multiple-Input Multiple-Output System Modelling using Convolved Gaussian Process Models Gang Cao • Edmund M-K Lai • Fakhrul Alam	2017-07-12
Large Scale Variable Fidelity Surrogate Modeling Evgeny Burnaev • Alexey Zaytsev	2017-07-12
Incorporating Uncertainty into Deep Learning for Spoken Language Assessment Andrey Malinin • Anton Ragni • Kate Knill • Mark Gales	2017-07-01
Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons Michael Burke • Siyabonga Mbonambi • Purity Molala • Raesetje Sefala	2017-06-19
Nudged elastic band calculations accelerated with Gaussian process regression Olli-Pekka Koistinen • Freyja B. Dagbjartsdóttir • Vilhjálmur Ásgeirsson • Aki Vehtari • Hannes Jónsson	2017-06-14
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes \| Eduardo C. Garrido-Merchán • Daniel Hernández-Lobato	2017-06-12
Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data Murat Seckin Ayhan • Vijay Raghavan • Alzheimer's disease Neuroimaging Initiative	2017-06-02
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis • Thomas F. W. Nicholson • Marc Peter Deisenroth • James Hensman	2017-05-30
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Zhenwen Dai • Mauricio A. Álvarez • Neil D. Lawrence	2017-05-27
Non-Stationary Spectral Kernels \| Sami Remes • Markus Heinonen • Samuel Kaski	2017-05-24
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model Ahmed M. Alaa • Jinsung Yoon • Scott Hu • Mihaela van der Schaar	2017-05-22
Streaming Sparse Gaussian Process Approximations \| Thang D. Bui • Cuong V. Nguyen • Richard E. Turner	2017-05-19
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization Kinjal Basu • Souvik Ghosh	2017-05-18
Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree Varun Kumar Ojha • Serena Schiano • Chuan-Yu Wu • Václav Snášel • Ajith Abraham	2017-05-16
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function Mahdi Barzegar Khalilsarai • Saeid Haghighatshoar • Giuseppe Caire • Gerhard Wunder	2017-05-09
Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models Gal Shulkind • Lior Horesh • Haim Avron	2017-05-02
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes Syed Ali Asad Rizvi • Stephen J. Roberts • Michael A. Osborne • Favour Nyikosa	2017-05-02
Multi-dueling Bandits with Dependent Arms Yanan Sui • Vincent Zhuang • Joel W. Burdick • Yisong Yue	2017-04-29
Structured Sparse Modelling with Hierarchical GP Danil Kuzin • Olga Isupova • Lyudmila Mihaylova	2017-04-27
Asynchronous Distributed Variational Gaussian Processes for Regression Hao Peng • Shandian Zhe • Yuan Qi	2017-04-22
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces Benjamin Paaßen • Christina Göpfert • Barbara Hammer	2017-04-21
Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian Processes Constantino A. García • Abraham Otero • Paulo Félix • Jesús Presedo • David G. Márquez	2017-04-14
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis Luca Ambrogioni • Eric Maris	2017-04-10
Joint Regression and Ranking for Image Enhancement Parag S. Chandakkar • Baoxin Li	2017-04-05
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations Eero Siivola • Aki Vehtari • Jarno Vanhatalo • Javier González • Michael Riis Andersen	2017-04-04
On Kernelized Multi-armed Bandits Sayak Ray Chowdhury • Aditya Gopalan	2017-04-03
Reliable Decision Support using Counterfactual Models Peter Schulam • Suchi Saria	2017-03-30
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions \| Hongqiao Wang • Jinglai Li	2017-03-29
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci • Philipp Hennig	2017-03-29
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations \| Maziar Raissi • Paris Perdikaris • George Em Karniadakis	2017-03-29
Discovering Latent Covariance Structures for Multiple Time Series Anh Tong • Jaesik Choi	2017-03-28
Fast and scalable Gaussian process modeling with applications to astronomical time series Daniel Foreman-Mackey • Eric Agol • Sivaram Ambikasaran • Ruth Angus	2017-03-28
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization Brett W. Israelsen • Nisar Ahmed • Kenneth Center • Roderick Green • Winston Bennett Jr	2017-03-27
Gaussian Processes with Context-Supported Priors for Active Object Localization Anthony D. Rhodes • Jordan Witte • Melanie Mitchell • Bruno Jedynak	2017-03-25
Distribution of Gaussian Process Arc Lengths Justin D. Bewsher • Alessandra Tosi • Michael A. Osborne • Stephen J. Roberts	2017-03-23
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks \| Guokun Lai • Wei-Cheng Chang • Yiming Yang • Hanxiao Liu	2017-03-21
Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed Noise Jakub Prüher • Filip Tronarp • Toni Karvonen • Simo Särkkä • Ondřej Straka	2017-03-15
Real-time 3D Human Tracking for Mobile Robots with Multisensors Mengmeng Wang • Daobilige Su • Lei Shi • Yong Liu • Jaime Valls Miro	2017-03-15
A Statistical Machine Learning Approach to Yield Curve Forecasting Rajiv Sambasivan • Sourish Das	2017-03-04
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process Sarfaraz Hussein • Robert Gillies • Kunlin Cao • Qi Song • Ulas Bagci	2017-03-02
Embarrassingly Parallel Inference for Gaussian Processes Michael Minyi Zhang • Sinead A. Williamson	2017-02-27
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings \| Sami Remes • Markus Heinonen • Samuel Kaski	2017-02-27
Semi-parametric Network Structure Discovery Models Amir Dezfouli • Edwin V. Bonilla • Richard Nock	2017-02-27
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs Mahdi Imani • Ulisses Braga-Neto	2017-02-24
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data \| Jiaxuan You • Xiaocheng Li • Melvin Low • David Lobell • Stefano Ermon	2017-02-12
Cluster-based Kriging Approximation Algorithms for Complexity Reduction Bas van Stein • Hao Wang • Wojtek Kowalczyk • Michael Emmerich • Thomas Bäck	2017-02-04
A Gaussian Process Regression Model for Distribution Inputs François Bachoc • Fabrice Gamboa • Jean-Michel Loubes • Nil Venet	2017-01-31
Patchwork Kriging for Large-scale Gaussian Process Regression Chiwoo Park • Daniel Apley	2017-01-23
Overlapping Cover Local Regression Machines Mohamed Elhoseiny • Ahmed Elgammal	2017-01-05
A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking for Head-Mounted Virtual Reality Systems Subarna Tripathi • Brian Guenter	2016-12-20
Towards Adaptive Training of Agent-based Sparring Partners for Fighter Pilots Brett W. Israelsen • Nisar Ahmed • Kenneth Center • Roderick Green • Winston Bennett Jr	2016-12-13
Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions Brett Israelsen • Nisar Ahmed	2016-12-13
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation Samir Bhatt • Ewan Cameron • Seth R Flaxman • Daniel J Weiss • David L Smith • Peter W Gething	2016-12-10
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker \| James H Cole • Rudra PK Poudel • Dimosthenis Tsagkrasoulis • Matthan WA Caan • Claire Steves • Tim D Spector • Giovanni Montana	2016-12-08
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization Valentin Dalibard • Michael Schaarschmidt • Eiko Yoneki	2016-12-01
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods Cristina Savin • Gasper Tkacik	2016-12-01
Probing the Compositionality of Intuitive Functions Eric Schulz • Josh Tenenbaum • David K. Duvenaud • Maarten Speekenbrink • Samuel J. Gershman	2016-12-01
Bayesian Optimization with Robust Bayesian Neural Networks \| Jost Tobias Springenberg • Aaron Klein • Stefan Falkner • Frank Hutter	2016-12-01
Incremental Variational Sparse Gaussian Process Regression Ching-An Cheng • Byron Boots	2016-12-01
Complex-valued Gaussian Process Regression for Time Series Analysis Luca Ambrogioni • Eric Maris	2016-11-30
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets Dario Azzimonti • David Ginsbourger • Clément Chevalier • Julien Bect • Yann Richet	2016-11-22
Probabilistic structure discovery in time series data David Janz • Brooks Paige • Tom Rainforth • Jan-Willem van de Meent • Frank Wood	2016-11-21
Bayesian optimization of hyper-parameters in reservoir computing \| Jan Yperman • Thijs Becker	2016-11-16
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging Jens Sjölund • Anders Eklund • Evren Özarslan • Hans Knutsson