Search Results for author: Barnabas Poczos

Found 76 papers, 25 papers with code

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

1 code implementation NeurIPS 2020 Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell

These results suggest that only the end of semantic processing of a word is task-dependent, and pose a challenge for future research to formulate new hypotheses for earlier task effects as a function of the task and stimuli.

Nonlinear ISA with Auxiliary Variables for Learning Speech Representations

no code implementations25 Jul 2020 Amrith Setlur, Barnabas Poczos, Alan W. black

This paper extends recent work on nonlinear Independent Component Analysis (ICA) by introducing a theoretical framework for nonlinear Independent Subspace Analysis (ISA) in the presence of auxiliary variables.

Speaker Verification

Covariate Distribution Aware Meta-learning

1 code implementation ICML Workshop LifelongML 2020 Amrith Setlur, Saket Dingliwal, Barnabas Poczos

Based on this model we propose a computationally feasible meta-learning algorithm by introducing meaningful relaxations in our final objective.

Few-Shot Learning

Politeness Transfer: A Tag and Generate Approach

1 code implementation ACL 2020 Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. black, Shrimai Prabhumoye

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.

Style Transfer TAG

Robust Density Estimation under Besov IPM Losses

no code implementations NeurIPS 2020 Ananya Uppal, Shashank Singh, Barnabas Poczos

We study minimax convergence rates of nonparametric density estimation in the Huber contamination model, in which a proportion of the data comes from an unknown outlier distribution.

Density Estimation

Optimal Exact Matrix Completion Under new Parametrization

no code implementations6 Feb 2020 Ilqar Ramazanli, Barnabas Poczos

We study the problem of exact completion for $m \times n$ sized matrix of rank $r$ with the adaptive sampling method.

Matrix Completion

Unsupervised Program Synthesis for Images By Sampling Without Replacement

no code implementations27 Jan 2020 Chenghui Zhou, Chun-Liang Li, Barnabas Poczos

However, they struggle with the inherent sparsity of meaningful programs in the search space.

Program Synthesis

Learned Interpolation for 3D Generation

no code implementations8 Dec 2019 Austin Dill, Songwei Ge, Eunsu Kang, Chun-Liang Li, Barnabas Poczos

The typical approach for incorporating this creative process is to interpolate in a learned latent space so as to avoid the problem of generating unrealistic instances by exploiting the model's learned structure.

Learning Local Search Heuristics for Boolean Satisfiability

1 code implementation NeurIPS 2019 Emre Yolcu, Barnabas Poczos

We present an approach to learn SAT solver heuristics from scratch through deep reinforcement learning with a curriculum.

reinforcement-learning Variable Selection

RotationOut as a Regularization Method for Neural Network

no code implementations18 Nov 2019 Kai Hu, Barnabas Poczos

We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction.

Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning

no code implementations22 Oct 2019 Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabas Poczos, Jay Whitacre, Venkatasubramanian Viswanathan

Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.

Developing Creative AI to Generate Sculptural Objects

no code implementations20 Aug 2019 Songwei Ge, Austin Dill, Eunsu Kang, Chun-Liang Li, Lingyao Zhang, Manzil Zaheer, Barnabas Poczos

We explore the intersection of human and machine creativity by generating sculptural objects through machine learning.

Generating 3D Point Clouds

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

2 code implementations5 Aug 2019 Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests.

Drug Discovery

A Deep Reinforcement Learning Approach for Global Routing

1 code implementation20 Jun 2019 Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabas Poczos, Kenji Shimada, Levent Burak Kara

At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning.

reinforcement-learning

Competence-based Curriculum Learning for Neural Machine Translation

1 code implementation NAACL 2019 Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell

In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance.

Machine Translation Translation

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

1 code implementation15 Mar 2019 Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.

Bayesian Optimisation

End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

no code implementations21 Feb 2019 Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabas Poczos, Emanuele Usai

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data.

General Classification

A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters

1 code implementation15 Feb 2019 Matthew Ho, Markus Michael Rau, Michelle Ntampaka, Arya Farahi, Hy Trac, Barnabas Poczos

Our first model, CNN$_\text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities.

Cosmology and Nongalactic Astrophysics

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

1 code implementation31 Jan 2019 Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.

Gaussian Processes Probabilistic Programming

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

2 code implementations19 Dec 2018 Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabas Poczos

Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.

Machine Translation Multimodal Sentiment Analysis +1

Point Cloud GAN

1 code implementation13 Oct 2018 Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, Ruslan Salakhutdinov

In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.

Object Recognition

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

no code implementations ICLR 2019 Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth.

End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC

no code implementations31 Jul 2018 Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabas Poczos

This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN.

General Classification

Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector

no code implementations28 Jun 2018 Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank Sciurba, Barnabas Poczos, Kayhan N. Batmanghelich

Our model consists of three mutually dependent modules which regulate each other: (1) a discriminative network that learns a fixed-length representation from local features and maps them to disease severity; (2) an attention mechanism that provides interpretability by focusing on the areas of the anatomy that contribute the most to the prediction task; and (3) a generative network that encourages the diversity of the local latent features.

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

1 code implementation25 May 2018 Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal.

Multi-Armed Bandits Probabilistic Programming +1

Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent

no code implementations13 Feb 2018 Yifan Wu, Barnabas Poczos, Aarti Singh

A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

1 code implementation NeurIPS 2018 Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

Bayesian Optimisation Model Selection +1

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima

no code implementations ICML 2018 Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh

We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation, i. e., $f(\mathbf{Z}, \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^T\mathbf{Z}_j)$, in which both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$ are parameters to be learned.

Estimating Cosmological Parameters from the Dark Matter Distribution

no code implementations6 Nov 2017 Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.

One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models

1 code implementation ICCV 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks.

Compressive Sensing Image Inpainting +1

A Generic Approach for Escaping Saddle points

no code implementations5 Sep 2017 Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alexander J. Smola

A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points.

Second-order methods

Gradient Descent Can Take Exponential Time to Escape Saddle Points

no code implementations NeurIPS 2017 Simon S. Du, Chi Jin, Jason D. Lee, Michael. I. Jordan, Barnabas Poczos, Aarti Singh

Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

1 code implementation25 May 2017 Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel.

Bayesian Optimisation

Data-driven Random Fourier Features using Stein Effect

no code implementations23 May 2017 Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabas Poczos

Large-scale kernel approximation is an important problem in machine learning research.

One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models

2 code implementations29 Mar 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.

Compressive Sensing Image Inpainting +1

Multi-fidelity Bayesian Optimisation with Continuous Approximations

no code implementations ICML 2017 Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabas Poczos

Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design.

Bayesian Optimisation

Deep Sets

2 code implementations NeurIPS 2017 Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola

Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.

Anomaly Detection Outlier Detection +1

CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding

1 code implementation8 Mar 2017 Francois Lanusse, Quanbin Ma, Nan Li, Thomas E. Collett, Chun-Liang Li, Siamak Ravanbakhsh, Rachel Mandelbaum, Barnabas Poczos

We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1. 4" and S/N larger than 20 on individual $g$-band LSST exposures.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies

The Statistical Recurrent Unit

2 code implementations ICML 2017 Junier B. Oliva, Barnabas Poczos, Jeff Schneider

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications.

Equivariance Through Parameter-Sharing

1 code implementation ICML 2017 Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

We propose to study equivariance in deep neural networks through parameter symmetries.

Hypothesis Transfer Learning via Transformation Functions

no code implementations NeurIPS 2017 Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos

We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain.

Transfer Learning

Variance Reduction in Stochastic Gradient Langevin Dynamics

no code implementations NeurIPS 2016 Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabas Poczos, Alexander J. Smola, Eric P. Xing

In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient.

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

no code implementations NeurIPS 2016 Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola

We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex.

Deep Learning with Sets and Point Clouds

no code implementations14 Nov 2016 Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set.

General Classification Outlier Detection

Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM

no code implementations11 Nov 2016 Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos

Due to numerical stability and quantifiability of the likelihood, RBM is commonly used with Bernoulli units.

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images

no code implementations19 Sep 2016 Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff Schneider, Barnabas Poczos

To this end, we study the application of deep conditional generative models in generating realistic galaxy images.

Stochastic Frank-Wolfe Methods for Nonconvex Optimization

no code implementations27 Jul 2016 Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola

Finally, we show that the faster convergence rates of our variance reduced methods also translate into improved convergence rates for the stochastic setting.

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization

no code implementations23 May 2016 Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola

This paper builds upon our recent series of papers on fast stochastic methods for smooth nonconvex optimization [22, 23], with a novel analysis for nonconvex and nonsmooth functions.

Fast Incremental Method for Nonconvex Optimization

no code implementations19 Mar 2016 Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola

We analyze a fast incremental aggregated gradient method for optimizing nonconvex problems of the form $\min_x \sum_i f_i(x)$.

Stochastic Variance Reduction for Nonconvex Optimization

no code implementations19 Mar 2016 Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola

We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them.

Stochastic Neural Networks with Monotonic Activation Functions

no code implementations1 Jan 2016 Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise.

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations

no code implementations NeurIPS 2015 Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins

We propose and analyse estimators for statistical functionals of one or moredistributions under nonparametric assumptions. Our estimators are derived from the von Mises expansion andare based on the theory of influence functions, which appearin the semiparametric statistics literature. We show that estimators based either on data-splitting or a leave-one-out techniqueenjoy fast rates of convergence and other favorable theoretical properties. We apply this framework to derive estimators for several popular informationtheoretic quantities, and via empirical evaluation, show the advantage of thisapproach over existing estimators.

Boolean Matrix Factorization and Noisy Completion via Message Passing

no code implementations28 Sep 2015 Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner

Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness.

Collaborative Filtering Matrix Completion

Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing

no code implementations4 Aug 2015 Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman

We formally characterize the power of popular tests for GDA like the Maximum Mean Discrepancy with the Gaussian kernel (gMMD) and bandwidth-dependent variants of the Energy Distance with the Euclidean norm (eED) in the high-dimensional MDA regime.

Two-sample testing

Bayesian Nonparametric Kernel-Learning

no code implementations29 Jun 2015 Junier Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.

An Analysis of Active Learning With Uniform Feature Noise

no code implementations15 May 2015 Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman

For larger $\sigma$, the \textit{unflattening} of the regression function on convolution with uniform noise, along with its local antisymmetry around the threshold, together yield a behaviour where noise \textit{appears} to be beneficial.

Active Learning

High Dimensional Bayesian Optimisation and Bandits via Additive Models

no code implementations5 Mar 2015 Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

We prove that, for additive functions the regret has only linear dependence on $D$ even though the function depends on all $D$ dimensions.

Additive models Bayesian Optimisation +1

On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives

no code implementations23 Nov 2014 Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman

The current literature is split into two kinds of tests - those which are consistent without any assumptions about how the distributions may differ (\textit{general} alternatives), and those which are designed to specifically test easier alternatives, like a difference in means (\textit{mean-shift} alternatives).

Two-sample testing

Learning Theory for Distribution Regression

1 code implementation8 Nov 2014 Zoltan Szabo, Bharath Sriperumbudur, Barnabas Poczos, Arthur Gretton

In this paper, we study a simple, analytically computable, ridge regression-based alternative to distribution regression, where we embed the distributions to a reproducing kernel Hilbert space, and learn the regressor from the embeddings to the outputs.

Density Estimation Learning Theory

On Estimating $L_2^2$ Divergence

no code implementations30 Oct 2014 Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

We give a comprehensive theoretical characterization of a nonparametric estimator for the $L_2^2$ divergence between two continuous distributions.

Fast Function to Function Regression

no code implementations27 Oct 2014 Junier Oliva, Willie Neiswanger, Barnabas Poczos, Eric Xing, Jeff Schneider

Function to function regression (FFR) covers a large range of interesting applications including time-series prediction problems, and also more general tasks like studying a mapping between two separate types of distributions.

Time Series Time Series Prediction

A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

no code implementations2 Oct 2014 Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Nicholas Battaglia, Barnabas Poczos, Jeff Schneider

In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.

Cosmology and Nongalactic Astrophysics

Nonparametric Estimation of Renyi Divergence and Friends

no code implementations12 Feb 2014 Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

We consider nonparametric estimation of $L_2$, Renyi-$\alpha$ and Tsallis-$\alpha$ divergences between continuous distributions.

Two-stage Sampled Learning Theory on Distributions

no code implementations7 Feb 2014 Zoltan Szabo, Arthur Gretton, Barnabas Poczos, Bharath Sriperumbudur

To the best of our knowledge, the only existing method with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which suffers from slow convergence issues in high dimensions), and the domain of the distributions to be compact Euclidean.

Density Estimation Learning Theory

Fast Distribution To Real Regression

no code implementations10 Nov 2013 Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

We study the problem of distribution to real-value regression, where one aims to regress a mapping $f$ that takes in a distribution input covariate $P\in \mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and outputs a real-valued response $Y=f(P) + \epsilon$.

FuSSO: Functional Shrinkage and Selection Operator

no code implementations10 Nov 2013 Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against.

A First Look at creating mock catalogs with machine learning techniques

no code implementations5 Mar 2013 Xiaoying Xu, Shirley Ho, Hy Trac, Jeff Schneider, Barnabas Poczos, Michelle Ntampaka

We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.

Cosmology and Nongalactic Astrophysics

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