Search Results for author:
Found 123 papers, 50 papers with code
In-n-Out: Calibrating Graph Neural Networks for Link Prediction
While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification.
Cooperative Bayesian Optimization for Imperfect Agents
We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function but have only control over one variable each.
Online Learning of Human Constraints from Feedback in Shared Autonomy
We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions.
Open Ad Hoc Teamwork with Cooperative Game Theory
Open ad hoc teamwork further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams.
Estimating treatment effects from single-arm trials via latent-variable modeling
Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
Bayesian Active Learning in the Presence of Nuisance Parameters
However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference.
Meta-Learning With Hierarchical Models Based on Similarity of Causal Mechanisms
In this work the goal is to generalise to new data in a non-iid setting where datasets from related tasks are observed, each generated by a different causal mechanism, and the test dataset comes from the same task distribution.
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD.
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible.
Input-gradient space particle inference for neural network ensembles
To sidestep these difficulties, we propose First-order Repulsive Deep Ensemble (FoRDE), an ensemble learning method based on ParVI, which performs repulsion in the space of first-order input gradients.
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
Learning relevant contextual variables within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers.
Online simulator-based experimental design for cognitive model selection
In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept for many multiagent settings is the Stackelberg equilibrium, under which a ``leader'' agent selects a strategy that maximizes its own payoff assuming the ``follower'' chooses their best response to this strategy.
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance between simulated and real data.
Differentiable User Models
Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop.
DPVIm: Differentially Private Variational Inference Improved
Using this as prior knowledge we establish a link between the gradients of the variational parameters, and propose an efficient while simple fix for the problem to obtain a less noisy gradient estimator, which we call $\textit{aligned}$ gradients.
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost.
Modular Flows: Differential Molecular Generation
Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis.
Provably expressive temporal graph networks
Specifically, novel constructions reveal the inadequacy of MP-TGNs and WA-TGNs, proving that neither category subsumes the other.
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function.
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers).
Tackling covariate shift with node-based Bayesian neural networks
In this paper, we interpret these latent noise variables as implicit representations of simple and domain-agnostic data perturbations during training, producing BNNs that perform well under covariate shift due to input corruptions.
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
For example, confidence intervals become too narrow, which we demonstrate with a simple experiment.
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs
Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human.
Parallel MCMC Without Embarrassing Failures
While efficient, this framework is very sensitive to the quality of subposterior sampling.
Zero-Shot Assistance in Sequential Decision Problems
We consider the problem of creating assistants that can help agents solve new sequential decision problems, assuming the agent is not able to specify the reward function explicitly to the assistant.
Deconfounded Representation Similarity for Comparison of Neural Networks
Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.
Approximate Bayesian Computation with Domain Expert in the Loop
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions.
Non-separable Spatio-temporal Graph Kernels via SPDEs
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs.
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead.
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustworthy.
De-randomizing MCMC dynamics with the diffusion Stein operator
Parallel to LD, Stein variational gradient descent (SVGD) similarly minimizes the KL, albeit endowed with a novel Stein-Wasserstein distance, by deterministically transporting a set of particle samples, thus de-randomizes the stochastic diffusion process.
Behaviour-conditioned policies for cooperative reinforcement learning tasks
We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner.
Learning to Assist Agents by Observing Them
On the other hand, offline data about the behavior of the assisted agent might be available, but is non-trivial to take advantage of by methods such as offline reinforcement learning.
Stochastic Cluster Embedding
In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE.
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
Targeted Active Learning for Bayesian Decision-Making
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
Affine Transport for Sim-to-Real Domain Adaptation
In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses.
D3p -- A Python Package for Differentially-Private Probabilistic Programming
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees.
Decision Rule Elicitation for Domain Adaptation
Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted results are to the target.
Sample-efficient reinforcement learning using deep Gaussian processes
In model-based reinforcement learning efficiency is improved by learning to simulate the world dynamics.
Differentially Private Bayesian Inference for Generalized Linear Models
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets.
Scalable Bayesian neural networks by layer-wise input augmentation
We introduce implicit Bayesian neural networks, a simple and scalable approach for uncertainty representation in deep learning.
Rethinking pooling in graph neural networks
In this paper, we build upon representative GNNs and introduce variants that challenge the need for locality-preserving representations, either using randomization or clustering on the complement graph.
Bayesian Inference for Optimal Transport with Stochastic Cost
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic data objects, that can be cast as probability measures.
Privacy-preserving Data Sharing on Vertically Partitioned Data
We modify a secure multiparty computation (MPC) framework to combine MPC with differential privacy (DP), in order to use differentially private MPC effectively to learn a probabilistic generative model under DP on such vertically partitioned data.
Teaching to Learn: Sequential Teaching of Agents with Inner States
In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model.
Differentially private cross-silo federated learning
In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting.
Likelihood-Free Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.
Human Strategic Steering Improves Performance of Interactive Optimization
To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function.
Federated Stochastic Gradient Langevin Dynamics
We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD).
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.
Correlated Feature Selection with Extended Exclusive Group Lasso
In many high dimensional classification or regression problems set in a biological context, the complete identification of the set of informative features is often as important as predictive accuracy, since this can provide mechanistic insight and conceptual understanding.
Informative Bayesian Neural Network Priors for Weak Signals
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.
Misspecification-robust likelihood-free inference in high dimensions
To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space.
Projective Preferential Bayesian Optimization
This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface.
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.
Interactive AI with a Theory of Mind
Understanding each other is the key to success in collaboration.
Probabilistic Formulation of the Take The Best Heuristic
The strengths of the probabilistic formulation, in addition to providing a bounded rational account of the learning of the heuristic, include natural extensibility with additional cognitively plausible constraints and prior information, and the possibility to embed the heuristic as a subpart of a larger probabilistic model.
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior.
Scalable Probabilistic Matrix Factorization with Graph-Based Priors
The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros.
Ranked #1 on
Recommendation Systems
on YahooMusic
(using extra training data)
Scalable Bayesian Non-linear Matrix Completion
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed.
Learning spectrograms with convolutional spectral kernels
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions.
Active Learning for Decision-Making from Imbalanced Observational Data
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).
Embarrassingly parallel MCMC using deep invertible transformations
While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge.
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics.
Representation Transfer for Differentially Private Drug Sensitivity Prediction
Differentially private learning with genomic data is challenging because it is more difficult to guarantee the privacy in high dimensions.
Learning Global Pairwise Interactions with Bayesian Neural Networks
Estimating global pairwise interaction effects, i. e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications.
Neural Non-Stationary Spectral Kernel
Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data.
Approximate Bayesian Computation via Population Monte Carlo and Classification
Approximate Bayesian computation (ABC) methods can be used to sample from posterior distributions when the likelihood function is unavailable or intractable, as is often the case in biological systems.
Harmonizable mixture kernels with variational Fourier features
The expressive power of Gaussian processes depends heavily on the choice of kernel.
Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function.
Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure.
Machine Teaching of Active Sequential Learners
We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses.
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.
Variational zero-inflated Gaussian processes with sparse kernels
We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent.
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
In human-in-the-loop machine learning, the user provides information beyond that in the training data.
ELFI: Engine for Likelihood-Free Inference
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
Non-Stationary Spectral Kernels
We propose non-stationary spectral kernels for Gaussian process regression.
Learning Image Relations with Contrast Association Networks
A key to the problem is learning a representation of relations.
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
Inverse Reinforcement Learning from Summary Data
Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting reinforcement learning models to behavioral data.
Differentially Private Bayesian Learning on Distributed Data
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects.
Distributed Bayesian Matrix Factorization with Limited Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models input-dependent couplings across multiple latent processes.
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features.
Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging.
Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction
The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters.
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.
Likelihood-free inference by ratio estimation
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.
Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals.
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.
Efficient differentially private learning improves drug sensitivity prediction
Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised.
Regression with n$\to$1 by Expert Knowledge Elicitation
We consider regression under the "extremely small $n$ large $p$" condition, where the number of samples $n$ is so small compared to the dimensionality $p$ that predictors cannot be estimated without prior knowledge.
Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.
Multi-view Kernel Completion
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels.
Sparse group factor analysis for biclustering of multiple data sources
Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments.
Visualizations Relevant to The User By Multi-View Latent Variable Factorization
The factorization is a generative model in which the display is parameterized as a part of the factorization and the other factors explain away the aspects not expressible in a two-dimensional display.
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA).
Learning Structures of Bayesian Networks for Variable Groups
We show that for dependency structures between groups to be expressible exactly, the data have to satisfy the so-called groupwise faithfulness assumption.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.
Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
Modelling-based experiment retrieval: A case study with gene expression clustering
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
Classification and Bayesian Optimization for Likelihood-Free Inference
This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.
Bayesian multi-tensor factorization
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors.
Group Factor Analysis
Factor analysis provides linear factors that describe relationships between individual variables of a data set.
Multiple Output Regression with Latent Noise
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal.
PinView: Implicit Feedback in Content-Based Image Retrieval
It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user.
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.
Toward computational cumulative biology by combining models of biological datasets
A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative.
Retrieval of Experiments by Efficient Estimation of Marginal Likelihood
To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements.
Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis
Results: In this paper, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on ge-nome-wide gene expression across several cancer cell lines (CMap database).
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects
To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model.
Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space
We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data.
Kernelized Bayesian Matrix Factorization
Our algorithm obtains the lowest Hamming loss values on 10 out of 14 multilabel classification data sets compared to five state-of-the-art multilabel learning algorithms.
RPA: Probabilistic analysis of probe performance and robust summarization
Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood.
