B. Póczos

发表

Gradient Descent Can Take Exponential Time to Escape Saddle Points

Michael I. Jordan, Barnabás Póczos, Chi Jin, 2017, NIPS.

k-NN Regression on Functional Data with Incomplete Observations

Barnabás Póczos, Sashank J. Reddi, B. Póczos, 2014, UAI.

On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives

Barnabás Póczos, Larry A. Wasserman, Aarti Singh, 2015, AISTATS.

Doubly Robust Covariate Shift Correction

Alexander J. Smola, Barnabás Póczos, Sashank J. Reddi, 2015, AAAI.

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

Barnabás Póczos, Larry A. Wasserman, Aarti Singh, 2014, ArXiv.

On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions

Barnabás Póczos, Larry A. Wasserman, Aarti Singh, 2014, AAAI.

Communication Efficient Coresets for Empirical Loss Minimization

Alexander J. Smola, Barnabás Póczos, Sashank J. Reddi, 2015, UAI.

Scale Invariant Conditional Dependence Measures

Barnabás Póczos, Sashank J. Reddi, B. Póczos, 2013, ICML.

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization pdf

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, ArXiv.

AIDE: Fast and Communication Efficient Distributed Optimization pdf

Alexander J. Smola, Barnabás Póczos, Peter Richtárik, 2016, ArXiv.

A Generic Approach for Escaping Saddle points

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2017, AISTATS.

Stochastic Variance Reduction for Nonconvex Optimization

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, ICML.

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, NIPS.

Fast Incremental Method for Nonconvex Optimization pdf

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, ArXiv.

Stochastic Frank-Wolfe methods for nonconvex optimization

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Fast incremental method for smooth nonconvex optimization

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

Variance Reduction in Stochastic Gradient Langevin Dynamics

Alexander J. Smola, Barnabás Póczos, Eric P. Xing, 2016, NIPS.

REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization

Barnabás Póczos, Csaba Szepesvári, Sergey Kirshner, 2010, AISTATS.

Budgeted Distribution Learning of Belief Net Parameters

Barnabás Póczos, Russell Greiner, Csaba Szepesvári, 2010, ICML.

Deep Learning with Sets and Point Clouds pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, ICLR.

DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING

Danica J. Sutherland, J. Schneider, B. Póczos, 2015, 1509.05409.

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations pdf

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, AISTATS.

Hypothesis Transfer Learning via Transformation Functions

Barnabás Póczos, Aarti Singh, Simon S. Du, 2016, NIPS.

Hierarchical Probabilistic Models for Group Anomaly Detection

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, AISTATS.

Linear-Time Learning on Distributions with Approximate Kernel Embeddings pdf

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2015, AAAI.

A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS

Danica J. Sutherland, J. Schneider, B. Póczos, 2014, 1410.0686.

Bayesian Nonparametric Kernel-Learning

Barnabás Póczos, Andrew Gordon Wilson, Eric P. Xing, 2015, AISTATS.

Regression on Probability Measures: A Simple and Consistent Algorithm

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2015 .

Deep Sets

Alexander J. Smola, Barnabás Póczos, Ruslan Salakhutdinov, 2017, 1703.06114.

FuSSO: Functional Shrinkage and Selection Operator pdf

Barnabás Póczos, Jeff G. Schneider, Aarti Singh, 2013, AISTATS.

On Estimating $L_2^2$ Divergence

L. Wasserman, Kirthevasan Kandasamy, B. Póczos, 2014, AISTATS 2015.

Copula-based Kernel Dependency Measures pdf

Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider, 2012, ICML.

Multi-fidelity Gaussian Process Bandit Optimisation

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, J. Artif. Intell. Res..

An Analysis of Active Learning with Uniform Feature Noise

Barnabás Póczos, Larry A. Wasserman, Aarti Singh, 2014, AISTATS.

Nonparametric kernel estimators for image classification

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Politeness Transfer: A Tag and Generate Approach

Yiming Yang, Alan W Black, Graham Neubig, 2020, ACL.

Distribution to Distribution Regression

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2013, ICML.

Learning when to stop thinking and do something!

Nathan R. Sturtevant, Barnabás Póczos, Russell Greiner, 2009, ICML '09.

The Statistical Recurrent Unit pdf

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2017, ICML.

On the Estimation of alpha-Divergences

Barnabás Póczos, Jeff G. Schneider, J. Schneider, 2011, AISTATS.

Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques

Barnabás Póczos, András Lörincz, A. Lörincz, 2009, J. Mach. Learn. Res..

Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

Barnabás Póczos, Shashank Singh, B. Póczos, 2016, NIPS.

Auto-regressive independent process analysis without combinatorial efforts

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2010, Pattern Analysis and Applications.

Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification

Barnabás Póczos, András Lörincz, Zoltán Ádám Milacski, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Collaborative Filtering via Group-Structured Dictionary Learning

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2012, LVA/ICA.

D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks pdf

Barnabás Póczos, András Lörincz, A. Lörincz, 2008, ArXiv.

Cross-Entropy Optimization for Independent Process Analysis

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2006, ICA.

Kalman-filtering using local interactions

Barnabás Póczos, András Lörincz, A. Lörincz, 2003, ArXiv.

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2015, ICML.

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, NIPS.

Nonparametric divergence estimators for independent subspace analysis

Barnabás Póczos, Jeff G. Schneider, Zoltán Szabó, 2011, 2011 19th European Signal Processing Conference.

Fast Distribution To Real Regression

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2013, AISTATS.

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, UAI.

Independent Subspace Analysis Using k-Nearest Neighborhood Distances

Barnabás Póczos, András Lörincz, A. Lörincz, 2005, ICANN.

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations pdf

James M. Robins, Kirthevasan Kandasamy, Akshay Krishnamurthy, 2014, ArXiv.

Minimax Distribution Estimation in Wasserstein Distance pdf

Barnabás Póczos, Shashank Singh, B. Póczos, 2018, ArXiv.

Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

Barnabás Póczos, Csaba Szepesvári, Dávid Pál, 2010, NIPS.

Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach

Brian L. DeCost, B. Póczos, A. Menon, 2017 .

Nonparametric Density Estimation under Adversarial Losses

Chun-Liang Li, Barnabás Póczos, Manzil Zaheer, 2018, NeurIPS.

Generalized Exponential Concentration Inequality for Renyi Divergence Estimation pdf

Barnabás Póczos, Shashank Singh, B. Póczos, 2014, ICML.

Learning to predict the cosmological structure formation

Wei Chen, Yin Li, Barnabás Póczos, 2018, Proceedings of the National Academy of Sciences.

CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding

B. Póczos, R. Mandelbaum, Siamak Ravanbakhsh, 2017, Monthly Notices of the Royal Astronomical Society.

ICA and ISA using Schweizer-Wolff measure of dependence

Barnabás Póczos, Sergey Kirshner, B. Póczos, 2008, ICML '08.

Group Anomaly Detection using Flexible Genre Models

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, NIPS.

Exponential Concentration of a Density Functional Estimator

Barnabás Póczos, Shashank Singh, B. Póczos, 2014, NIPS.

A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra

Barnabás Póczos, Russell Greiner, Siamak Ravanbakhsh, 2010, AAAI.

Separation theorem for independent subspace analysis and its consequences

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2012, Pattern Recognit..

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

Barnabás Póczos, Nematollah Batmanghelich, Mingming Gong, 2018, MICCAI.

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

Kirthevasan Kandasamy, Barnabás Póczos, Biswajit Paria, 2019, Cell Reports Physical Science.

Machine learning-aided modeling of fixed income instruments

Barnabás Póczos, Daniel Martin, B. Póczos, 2018 .

Cheminformatics for accelerated design of chemical admixtures

Barnabás Póczos, Christopher M. Childs, Oğulcan Canbek, 2020 .

Molecular Engineering of Superplasticizers for Metakaolin‐Portland Cement Blends with Hierarchical Machine Learning

Christopher M. Childs, B. Póczos, K. Kurtis, 2018, Advanced Theory and Simulations.

Gaussian Process Optimisation with Multi-fidelity Evaluations

Junier B. Oliva, J. Schneider, Kirthevasan Kandasamy, 2017 .

Connoisseur : Can GANs Learn Simple 1 D Parametric Distributions ?

M. Zaheer, B. Póczos, 2018 .

Generative Adversarial Image Refinement for Handwriting Recognition

B. Póczos, D. Dilipkumar, 2017 .

Proposal Estimating Probability Distributions and their Properties

B. Póczos, Shashank Singh, 2018 .

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

Paul Pu Liang, B. Póczos, Louis-Philippe Morency, 2018 .

Cost-Aware Bayesian Optimization via Information Directed Sampling

J. Schneider, B. Póczos, W. Neiswanger, 2020 .

Nonparametric Density Estimation under Adversarial Losses

M. Zaheer, B. Póczos, Chun-Liang Li, 2018, NeurIPS.

M L ] 3 0 Ja n 20 18 Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, A. G. Wilson, E. Xing, 2018 .

Distribution Learning of Belief Net Parameters

R. Greiner, B. Póczos, Liuyang Li, 2015 .

Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks

B. Póczos, Shashank Singh, Jian Ma, 2016, bioRxiv.

Active learning and search on low-rank matrices

Barnabás Póczos, Jeff G. Schneider, Dougal J. Sutherland, 2013, KDD.

Rates of Convergence of Nonparametric Estimators for Model Shift

B. Póczos, S. Jayanth, Koushik Aarti Singh, 2016 .

On Estimating L22 Divergence

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2015, AISTATS.

Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization

Jay F. Whitacre, Kirthevasan Kandasamy, Barnabás Póczos, 2020 .

Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2015, International Joint Conference on Artificial Intelligence.

Learning Robust Joint Representations for Multimodal Sentiment Analysis

Barnabás Póczos, Louis-Philippe Morency, Paul Pu Liang, 2018 .

Non-negative matrix factorization extended by sparse code shrinkage and weight sparsification non-negative matrix factorization algorithms

Barnabás Póczos, András Lörincz, Julian Eggert, 2002, ECAI.

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis pdf

Barnabás Póczos, Paul Pu Liang, Hai Pham, 2018, ArXiv.

Minimax Rates of Distribution Estimationin Wasserstein Distance

B. Póczos, Shashank Singh, 2019 .

Anomaly Detection for Astronomical Data

Barnabás Póczos, Andrew J. Connolly, Jeff Schneider, 2010 .

12-2011 Robust Nonparametric Copula Based Dependence Estimators

B. Póczos, 2015 .

Robust Nonparametric Copula Based Dependence Estimators

Csaba Szepesvari, D. Pál, J. Schneider, 2011 .

Vector-valued distribution regression: a simple and consistent approach

Bharath K. Sriperumbudur, B. Póczos, A. Gretton, 2014 .

Consistent Vector-valued Regression on Probability Measures

Bharath K. Sriperumbudur, B. Póczos, A. Gretton, 2015 .

Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses

B. Póczos, Shashank Singh, A. Uppal, 2019, NeurIPS.

On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions

Sashank J. Reddi, L. Wasserman, Aarti Singh, 2014, AAAI.

Machine Learning Department School of Computer Science 2012 Support Distribution Machines

Danica J. Sutherland, J. Schneider, B. Póczos, 2015 .

CDS&E: Collaborative Research: Machine Learning for Automated Discovery and Control in Turbulent Plasma

J. Schneider, B. Póczos, H. Trac, 2015 .

End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data

B. Póczos, M. Paulini, J. Alison, 2019, 1910.07029.

Exploring End-to-end Deep Learning Applications for Event Classification at CMS

B. Póczos, M. Paulini, S. Gleyzer, 2019, EPJ Web of Conferences.

End-to-End Event Classification of High-Energy Physics Data

S. V. Gleyzer, Barnabas Poczos, Manfred Paulini, 2018, Journal of Physics: Conference Series.

on CMS information server CMS CR-2018 / 379 The Compact Muon Solenoid Experiment

B. Póczos, M. Paulini, S. Gleyzer, 2018 .

Understanding the relationship between Functional and Structural Connectivity of Brain Networks

Sashank J. Reddi, Aarti Singh, B. Póczos, 2015 .

Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions

Sashank J. Reddi, L. Wasserman, Aarti Singh, 2014, ArXiv.

On the Estimation of α-Divergences

J. Schneider, B. Póczos, 2011 .

Query efficient posterior estimation in scientific experiments via Bayesian active learning

J. Schneider, Kirthevasan Kandasamy, B. Póczos, 2017, Artif. Intell..

Independent Process Analysis Without a Priori Dimensional Information

B. Póczos, Z. Szabó, M. Kiszlinger, 2007, ICA.

Conditional Distance Variance and Correlation

Barnabás Póczos, Jeff Schneider, J. Schneider, 2012 .

Nonparametric Density Estimation&Convergence Rates for GANs under Besov IPM Losses

B. Póczos, Shashank Singh, A. Uppal, 2019, 1902.03511.

Multiset multicover methods for discriminative marker selection

B. Póczos, Z. Bar-Joseph, A. Alavi, 2022, Cell reports methods.

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology

Barnabas Poczos, B. Póczos, 2018 .

Minimizing FLOPs to Learn Efficient Sparse Representations pdf

Barnabás Póczos, Pradeep Ravikumar, Biswajit Paria, 2020, ICLR.

Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning

Barnabás Póczos, Jian Ma, Shashank Singh, 2018, AISTATS.

On the Reconstruction Risk of Convolutional Sparse Dictionary Learning pdf

Barnabás Póczos, Jian Ma, Shashank Singh, 2017, ArXiv.

Near-Orthogonality Regularization in Kernel Methods

Pengtao Xie, Barnabás Póczos, Eric P. Xing, 2017, UAI.

Stochastic Neural Networks with Monotonic Activation Functions

Dale Schuurmans, Barnabás Póczos, Jeff G. Schneider, 2016, AISTATS.

Competitive spiking and indirect entropy minimization of rate code: Efficient search for hidden components

Barnabás Póczos, András Lőrincz, Botond Szatmáry, 2004, Journal of Physiology-Paris.

Ockham's Razor at Work: Modeling of the ``Homunculus''

Barnabás Póczos, András Lörincz, Bálint Takács, 2002 .

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming pdf

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, ArXiv.

Competence-based Curriculum Learning for Neural Machine Translation

Barnabás Póczos, Tom M. Mitchell, Graham Neubig, 2019, NAACL.

MMD GAN: Towards Deeper Understanding of Moment Matching Network

Yiming Yang, Yu Cheng, Chun-Liang Li, 2017, NIPS.

Gradient Descent Provably Optimizes Over-parameterized Neural Networks pdf

Barnabás Póczos, Aarti Singh, Simon S. Du, 2018, ICLR.

Nonparametric independent process analysis

Barnabás Póczos, Zoltán Szabó, B. Póczos, 2011, 2011 19th European Signal Processing Conference.

Post Nonlinear Independent Subspace Analysis

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2007, ICANN.

Undercomplete Blind Subspace Deconvolution

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2007, J. Mach. Learn. Res..

Theorem for K-Independent Subspace Analysis with Sufficient Conditions

Barnabas Poczos, Andras Lorincz, Zoltan Szabo, 2006, math/0608100.

Independent Subspace Analysis on Innovations

Barnabás Póczos, András Lörincz, Bálint Takács, 2005, ECML.

Independent subspace analysis using geodesic spanning trees

Barnabás Póczos, András Lörincz, B. Póczos, 2005, ICML.

Separation Theorem for Independent Subspace Analysis

Barnabás Póczos, Zoltán Szabó, András Lőrincz, 2005 .

Cost Component Analysis

Barnabás Póczos, András Lörincz, B. Póczos, 2003, Int. J. Neural Syst..

Non-negative matrix factorization extended by sparse code shrinkage and by weight sparsi £ cation

B. Póczos, J. Eggert, E. Körner, 2003 .

Non-negative matrix factorization extended by sparse code shrinkage and by weight sparsification

Barnabás Póczos, Julian Eggert, Edgar Körner, 2002 .

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

Kirthevasan Kandasamy, Biswajit Paria, Venkatasubramanian Viswanathan, 2019, Cell Reports Physical Science.

Predicting enhancer-promoter interaction from genomic sequence with deep neural networks

Barnabás Póczos, Jian Ma, Shashank Singh, 2016, bioRxiv.

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

Eric Xing, Kirthevasan Kandasamy, Willie Neiswanger, 2019 .

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization pdf

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, ArXiv.

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

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, J. Mach. Learn. Res..

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers.

Barnabás Póczos, Christopher M. Childs, Philip R LeDuc, 2020, ACS biomaterials science & engineering.

Developing Creative AI to Generate Sculptural Objects pdf

Chun-Liang Li, Barnabás Póczos, Manzil Zaheer, 2019, ArXiv.

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems

Barnabás Póczos, Jeff G. Schneider, Xuezhi Wang, 2016, IJCAI.

Learning Theory for Distribution Regression

Arthur Gretton, Zoltan Szabo, Barnabas Poczos, 2014, J. Mach. Learn. Res..

Neural Architecture Search with Bayesian Optimisation and Optimal Transport pdf

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2018, NeurIPS.

Nonparametric Estimation of Renyi Divergence and Friends

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2014, ICML.

Nonparametric Estimation of Conditional Information and Divergences

Barnabás Póczos, Jeff G. Schneider, J. Schneider, 2012, AISTATS.

Support Distribution Machines pdf

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2012, ArXiv.

Nonparametric Divergence Estimation and its Applications to Machine Learning

Barnabás Póczos, Jeff Schneider, Liang Xiong, 2011 .

Fast Function to Function Regression pdf

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2014, AISTATS.

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

Michael Andrews, Barnabas Poczos, Manfred Paulini, 2018, Computing and Software for Big Science.

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

Tom M. Mitchell, Barnabas Poczos, Leila Wehbe, 2020, NeurIPS.

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

Chun-Liang Li, Aswin C. Sankaranarayanan, Barnabas Poczos, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

Barnabás Póczos, Michael Andrews, Sitong An, 2019, ArXiv.

Equivariance Through Parameter-Sharing pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2017, ICML.

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

Ruosong Wang, Barnabás Póczos, Ruslan Salakhutdinov, 2019, NeurIPS.

Robust Plant Phenotyping via Model-Based Optimization

Barnabás Póczos, David Wettergreen, Paloma Sodhi, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

Barnabás Póczos, Larry A. Wasserman, Aarti Singh, 2015, ArXiv.

Learning Local Search Heuristics for Boolean Satisfiability

Barnabás Póczos, Emre Yolcu, B. Póczos, 2019, NeurIPS.

Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints

Barnabás Póczos, Aarti Singh, Fang-Cheng Yeh, 2016, bioRxiv.

Point Cloud GAN pdf

Yang Zhang, Chun-Liang Li, Barnabás Póczos, 2018, DGS@ICLR.

Efficient Meta Lifelong-Learning with Limited Memory pdf

Barnabás Póczos, Sanket Vaibhav Mehta, Jaime Carbonell, 2020, EMNLP.

Characterizing and Avoiding Negative Transfer

Jaime G. Carbonell, Barnabás Póczos, Zihang Dai, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Classifier Two Sample Test for Video Anomaly Detections

Yusha Liu, Chun-Liang Li, Barnabás Póczos, 2018, BMVC.

Parallelised Bayesian Optimisation via Thompson Sampling

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, AISTATS.

Deep generative models for galaxy image simulations

B. Póczos, P. Freeman, R. Mandelbaum, 2020, Monthly Notices of the Royal Astronomical Society.

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

Yuandong Tian, Barnabás Póczos, Jason D. Lee, 2017, ICML.

The Role of Machine Learning in the Next Decade of Cosmology

Aneta Siemiginowska, Shy Genel, Michelle Lochner, 2019, 1902.10159.

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, AAAI.

Non-combinatorial estimation of independent autoregressive sources

Barnabás Póczos, András Lörincz, B. Póczos, 2006, Neurocomputing.

Neural Adaptation of the Kalman-Gain

Barnabás Póczos, Gábor Szirtes, L Andras, 2004 .

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2017, ICML.

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2019, ICML.

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

Barnabás Póczos, Louis-Philippe Morency, Paul Pu Liang, 2018, AAAI.

A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

J. Schneider, B. Póczos, S. Ho, 2013, 1303.1055.

Distribution-Free Distribution Regression pdf

Barnabás Póczos, Larry A. Wasserman, Alessandro Rinaldo, 2013, AISTATS.

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

B. Póczos, M. Ntampaka, H. Trac, 2019, The Astrophysical Journal.

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

Chun-Liang Li, Barnabás Póczos, Siamak Ravanbakhsh, 2016, ArXiv.

Contextual Parameter Generation for Knowledge Graph Link Prediction

Barnabás Póczos, Tom M. Mitchell, George Stoica, 2020, AAAI.

LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds

Yaser Sheikh, Chun-Liang Li, Barnabás Póczos, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Estimating Cosmological Parameters from the Dark Matter Distribution pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, ICML.

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations

James M. Robins, Kirthevasan Kandasamy, Akshay Krishnamurthy, 2015, NIPS.

High-Throughput Robotic Phenotyping of Energy Sorghum Crops

Barnabás Póczos, David Wettergreen, Simon S. Du, 2017, FSR.

Local Connectome Fingerprinting Reveals the Uniqueness of Individual White Matter Architecture

Barnabás Póczos, Aarti Singh, Fang-Cheng Yeh, 2016 .

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, 2016, AISTATS.

Nonparametric distribution regression applied to sensor modeling

Barnabás Póczos, Alonzo Kelly, Abhijeet Tallavajhula, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Boolean Matrix Factorization and Noisy Completion via Message Passing

Barnabás Póczos, Russell Greiner, Siamak Ravanbakhsh, 2015, ICML.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling pdf

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2017, ArXiv.

Nonparametric Density Estimation under Besov IPM Losses pdf

Barnabás Póczos, Shashank Singh, Ananya Uppal, 2019, ArXiv.

Two-stage sampled learning theory on distributions

Bharath K. Sriperumbudur, B. Póczos, A. Gretton, 2014, AISTATS.

Bayesian active learning for posterior estimation

Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, 2015 .

Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data

James H. Garrett, Barnabás Póczos, Mario Berges, 2015, Adv. Eng. Informatics.

End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC pdf

Barnabás Póczos, Michael Andrews, Manfred Paulini, 2018, ArXiv.

Robust Density Estimation under Besov IPM Losses

B. Póczos, Shashank Singh, Ananya Uppal, 2020, 2004.08597.

A Deep Reinforcement Learning Approach for Global Routing pdf

Levent Burak Kara, Barnabás Póczos, Kenji Shimada, 2019, ArXiv.

Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods

Chun-Liang Li, Barnabás Póczos, B. Póczos, 2016, UAI.

RotationOut as a Regularization Method for Neural Network pdf

Kai Hu, Barnabas Poczos, B. Póczos, 2019, ArXiv.

Vector-valued Distribution Regression - Keep It Simple and Consistent

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2015 .

Data-driven Random Fourier Features using Stein Effect

Yiming Yang, Chun-Liang Li, Barnabás Póczos, 2017, IJCAI.

Transformation Autoregressive Networks

Barnabás Póczos, Eric P. Xing, Ruslan Salakhutdinov, 2018, ICML.

Recurrent Estimation of Distributions pdf

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2017, ArXiv.

Robust Handwriting Recognition with Limited and Noisy Data

Kang Huang, Hai Pham, Amrith Setlur, 2020, 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR).

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

Alexander J. Smola, Barnabás Póczos, Suvrit Sra, 2015, NIPS.

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

Kirthevasan Kandasamy, Barnabás Póczos, Biswajit Paria, 2018, UAI.

Online Dictionary Learning with Group Structure Inducing Norms

Barnabás Póczos, András Lőrincz, Zoltán Szabó, 2011, ICML 2011.

Online group-structured dictionary learning

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2011, CVPR 2011.

Machine learning approaches to admixture design for clay-based cements

Barnabás Póczos, Christopher M. Childs, Kimberly E. Kurtis, 2018 .

Kernel Change-point Detection with Auxiliary Deep Generative Models

B. Póczos, Yiming Yang, Wei-Cheng Chang, 2018, ICLR.

Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE

B. Póczos, Chenghui Zhou, 2023, ArXiv.

Implicit Kernel Learning pdf

Yiming Yang, Chun-Liang Li, Barnabás Póczos, 2019, AISTATS.

Minimax Estimation of Quadratic Fourier Functionals pdf

Barnabás Póczos, Shashank Singh, Bharath K. Sriperumbudur, 2018, ArXiv.

On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions

Sashank J. Reddi, L. Wasserman, Aarti Singh, 2014, AAAI.

Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE

B. Póczos, Chenghui Zhou, 2023, ArXiv.

Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool

Z. Szabo, A. Lorincz, G. Hevizi, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

The Multi-fidelity Multi-armed Bandit

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, NIPS.

BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals

Nikos D. Sidiropoulos, Hyun Ah Song, Christos Faloutsos, 2017, SDM.

Robust Density Estimation under Besov IPM Losses pdf

Shashank Singh, Barnabas Poczos, Ananya Uppal, 2020, NeurIPS.

Efficient Nonparametric Smoothness Estimation

Barnabás Póczos, Simon S. Du, Shashank Singh, 2016, NIPS.

Collaborative Filtering via Group-Structured Dictionary Learning

B. Póczos, Z. Szabó, András Lörincz, 2012, LVA/ICA.

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

B. Póczos, M. Paulini, S. Gleyzer, 2020, Computing and Software for Big Science.

The student becomes the master: Matching GPT3 on Scientific Factual Error Correction

B. Póczos, Hai Pham, D. Ashok, 2023, ArXiv.

Undercomplete Blind Subspace Deconvolution Via Linear Prediction

Barnabás Póczos, András Lörincz, Zoltán Szabó, 2007, ECML.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers.

Christopher M. Childs, B. Póczos, A. Feinberg, 2020, ACS biomaterials science & engineering.

Learning Theory for Vector-Valued Distribution Regression

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2015 .

Two-stage sampled learning theory on distributions

Bharath K. Sriperumbudur, B. Póczos, A. Gretton, 2014, AISTATS.

Improving Molecule Properties Through 2-Stage VAE

B. Póczos, Chenghui Zhou, 2022, ArXiv.

Covariate Distribution Aware Meta-learning

Amrith Rajagopal Setlur, B. Póczos, Saket Dingliwal, 2020, ArXiv.

Unsupervised program synthesis for images by sampling without replacement

Barnabas Poczos, Chun-Liang Li, Chenghui Zhou, 2020, UAI.

Unsupervised Program Synthesis for Images using Tree-Structured LSTM pdf

Chun-Liang Li, Barnabas Poczos, Chenghui Zhou, 2020, ArXiv.