Barnabás Póczos

发表

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

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

High Dimensional Bayesian Optimisation and Bandits via Additive Models

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

Cheminformatics for accelerated design of chemical admixtures

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

Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques

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

Communication Efficient Coresets for Empirical Loss Minimization

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

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

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

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

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

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

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

Machine learning-aided modeling of fixed income instruments

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

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems

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

Nonparametric divergence estimators for independent subspace analysis

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

An Analysis of Active Learning with Uniform Feature Noise

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

Cautious Deep Learning pdf

Barnabás Póczos, Larry A. Wasserman, Yotam Hechtlinger, 2018, ArXiv.

Characterizing and Avoiding Negative Transfer

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

ICA and ISA using Schweizer-Wolff measure of dependence

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

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

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

Separation theorem for independent subspace analysis and its consequences

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

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.

Gradient Descent Can Take Exponential Time to Escape Saddle Points

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

Recurrent Estimation of Distributions pdf

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

Kalman-filtering using local interactions

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

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

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

Learning when to stop thinking and do something!

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

MMD GAN: Towards Deeper Understanding of Moment Matching Network

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

Estimating Cosmological Parameters from the Dark Matter Distribution pdf

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

Cost Component Analysis

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

Auto-regressive independent process analysis without combinatorial efforts

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

Stochastic Variance Reduction for Nonconvex Optimization

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

Cross-Entropy Optimization for Independent Process Analysis

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

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

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

Independent Subspace Analysis Using k-Nearest Neighborhood Distances

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

Nonparametric Density Estimation under Adversarial Losses

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

Efficient Nonparametric Smoothness Estimation

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

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).

Machine learning approaches to admixture design for clay-based cements

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

A Generic Approach for Escaping Saddle points

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

Regression on Probability Measures: A Simple and Consistent Algorithm

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

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

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

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

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

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).

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.

Minimax Distribution Estimation in Wasserstein Distance pdf

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

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

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

Undercomplete Blind Subspace Deconvolution

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

Hallucinating Point Cloud into 3D Sculptural Object pdf

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

Query efficient posterior estimation in scientific experiments via Bayesian active learning

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2017, Artif. Intell..

VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing

Barnabás Póczos, András Lörincz, Zoltán Ádám Milacski, 2020, ICML.

Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning

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

Learning Robust Joint Representations for Multimodal Sentiment Analysis

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

AIDE: Fast and Communication Efficient Distributed Optimization pdf

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

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

Yifan Wu, Barnabás Póczos, Aarti Singh, 2019, AISTATS.

Generalized Exponential Concentration Inequality for Renyi Divergence Estimation pdf

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

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).

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

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

Transformation Function Based Methods for Model Shift pdf

Barnabás Póczos, Aarti Singh, Simon Shaolei Du, 2016, ArXiv.

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization pdf

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

Exponential Concentration of a Density Functional Estimator

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

Transformation Autoregressive Networks

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

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

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

The Multi-fidelity Multi-armed Bandit

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

Point Cloud GAN pdf

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

Parallelised Bayesian Optimisation via Thompson Sampling

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

Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet

Barnabás Póczos, András Lőrincz, Zoltán Á. Milacski, 2019, BMVC.

Multi-fidelity Gaussian Process Bandit Optimisation

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

Distribution Regression with Minimax-Optimal Guarantee

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2016 .

Learning Local Search Heuristics for Boolean Satisfiability

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

Collaborative Filtering via Group-Structured Dictionary Learning

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

Equivariance Through Parameter-Sharing pdf

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

Learning Theory for Vector-Valued Distribution Regression

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

Budgeted Distribution Learning of Belief Net Parameters

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

Nonparametric kernel estimators for image classification

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

Neural Architecture Search with Bayesian Optimisation and Optimal Transport pdf

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

Gradient Descent Provably Optimizes Over-parameterized Neural Networks pdf

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

Efficient Learning on Point Sets

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2013, 2013 IEEE 13th International Conference on Data Mining.

Hypothesis Transfer Learning via Transformation Functions

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

Fast Distribution To Real Regression

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

Consistent Distribution Regression via Mean Embedding

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2014 .

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).

Stochastic Neural Networks with Monotonic Activation Functions

Dale Schuurmans, Barnabás Póczos, Jeff G. Schneider, 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).

Implicit Kernel Learning pdf

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

Separation Theorem for Independent Subspace Analysis

Barnabás Póczos, Zoltán Szabó, András Lőrincz, 2005 .

Non-combinatorial estimation of independent autoregressive sources

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

A Deep Reinforcement Learning Approach for Global Routing pdf

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

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.

Post Nonlinear Independent Subspace Analysis

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

Bayesian Nonparametric Kernel-Learning

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

Nonparametric Estimation of Conditional Information and Divergences

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

Online group-structured dictionary learning

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

Nonparametric Estimation of Renyi Divergence and Friends

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

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

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

On the Estimation of alpha-Divergences

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

Nonparametric independent process analysis

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

Competence-based Curriculum Learning for Neural Machine Translation

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

Distribution-Free Distribution Regression pdf

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

Linear-Time Learning on Distributions with Approximate Kernel Embeddings pdf

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

Local Connectome Fingerprinting Reveals the Uniqueness of Individual White Matter Architecture

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

Learning to predict the cosmological structure formation

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

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

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

Data-driven Random Fourier Features using Stein Effect

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

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

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

Developing Creative AI to Generate Sculptural Objects pdf

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

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations

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

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

Barnabás Póczos, Julian Eggert, Edgar Körner, 2002 .

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

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

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.

Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding pdf

Barnabás Póczos, Arthur Gretton, Zoltán Szabó, 2014, ArXiv.

Minimax Estimation of Quadratic Fourier Functionals pdf

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

FuSSO: Functional Shrinkage and Selection Operator pdf

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

Multi-fidelity Bayesian Optimisation with Continuous Approximations

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

Nonparametric Divergence Estimation and its Applications to Machine Learning

Barnabás Póczos, Jeff Schneider, Liang Xiong, 2011 .

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

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

Minimizing FLOPs to Learn Efficient Sparse Representations pdf

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

Efficient Meta Lifelong-Learning with Limited Memory pdf

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

k-NN Regression on Functional Data with Incomplete Observations

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

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

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

The Statistical Recurrent Unit pdf

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

Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods

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

Deep Mean Maps pdf

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

Fast Incremental Method for Nonconvex Optimization pdf

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

Distribution to Distribution Regression

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

Deep Sets

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

Kernel Change-point Detection with Auxiliary Deep Generative Models pdf

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

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

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

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

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

Hierarchical Probabilistic Models for Group Anomaly Detection

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

Online Dictionary Learning with Group Structure Inducing Norms

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

On the Reconstruction Risk of Convolutional Sparse Dictionary Learning pdf

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

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

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

Independent Process Analysis Without a Priori Dimensional Information

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

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

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

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

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

Variance Reduction in Stochastic Gradient Langevin Dynamics

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

Undercomplete Blind Subspace Deconvolution Via Linear Prediction

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

Contextual Parameter Generation for Knowledge Graph Link Prediction

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

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

Barnabás Póczos, András Lörincz, Bálint Takács, 2002 .

Independent subspace analysis using geodesic spanning trees

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

Near-Orthogonality Regularization in Kernel Methods

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

Scale Invariant Conditional Dependence Measures

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

Neural Adaptation of the Kalman-Gain

Barnabás Póczos, Gábor Szirtes, L Andras, 2004 .

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis pdf

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

Support Distribution Machines pdf

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

Independent Subspace Analysis on Innovations

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

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.

Two-stage sampled learning theory on distributions

Barnabás Póczos, Arthur Gretton, Zoltán Szabó, 2015, AISTATS.

Active learning and search on low-rank matrices

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

High-Throughput Robotic Phenotyping of Energy Sorghum Crops

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

Group Anomaly Detection using Flexible Genre Models

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

Fast Function to Function Regression pdf

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

Distribution Regression - the Set Kernel Heuristic is Consistent

Barnabás Póczos, Arthur Gretton, Bharath K. Sriperumbudur, 2014 .

Nonparanormal Information Estimation pdf

Barnabás Póczos, Shashank Singh, 2017, ICML.

Copula-based Kernel Dependency Measures pdf

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

Bayesian active learning for posterior estimation

Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, 2015 .

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

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

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

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

Neural Kalman filter

Barnabás Póczos, András Lörincz, Gábor Szirtes, 2005, Neurocomputing.

BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals

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

Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation pdf

Barnabás Póczos, Shashank Singh, 2016, ArXiv.

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.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling pdf

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

Vector-valued Distribution Regression - Keep It Simple and Consistent

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

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

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

On Estimating L22 Divergence

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

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

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

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

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

Fast incremental method for smooth nonconvex optimization

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

Deep Learning with Sets and Point Clouds pdf

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

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.

Boolean Matrix Factorization and Noisy Completion via Message Passing

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

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

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

Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization

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

Classifier Two Sample Test for Video Anomaly Detections

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

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

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

Doubly Robust Covariate Shift Correction

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

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations pdf

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

Nonparametric Density Estimation under Besov IPM Losses pdf

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

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

Barnabás Póczos, Fang-Cheng Yeh, Timothy D. Verstynen, 2016 .

Learning on Distributions

Barnabás Póczos, Arthur Gretton, Zoltán Szabó, 2014 .

Conditional Distance Variance and Correlation

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

Anomaly Detection for Astronomical Data

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