Poczos, Barnabas

102 publications

ICLR 2025 Chemistry-Inspired Diffusion with Non-Differentiable Guidance Yuchen Shen, Chenhao Zhang, Sijie Fu, Chenghui Zhou, Newell Washburn, Barnabas Poczos

ICLR 2025 Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention Tongzhou Liao, Barnabas Poczos

ICMLW 2024 GraphBPE: Molecular Graphs Meet Byte-Pair Encoding Yuchen Shen, Barnabas Poczos

ICMLW 2024 Non-Differentiable Diffusion Guidance for Improved Molecular Geometry Yuchen Shen, Chenhao Zhang, Chenghui Zhou, Sijie Fu, Newell Washburn, Barnabas Poczos

NeurIPSW 2023 $\mathbb{S}$ci$\mathbb{F}$ix: Outperforming GPT3 on Scientific Factual Error Correction Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabas Poczos

AAAI 2021 Re-TACRED: Addressing Shortcomings of the TACRED Dataset George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

UAI 2021 Unsupervised Program Synthesis for Images by Sampling Without Replacement Chenghui Zhou, Chun-Liang Li, Barnabás Póczos

AISTATS 2020 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

AAAI 2020 Contextual Parameter Generation for Knowledge Graph Link Prediction George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos

ICMLW 2020 Covariate Distribution Aware Meta-Learning Amrith Setlur, Saket Dingliwal, Barnabas Poczos

ICLR 2020 Minimizing FLOPs to Learn Efficient Sparse Representations Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

NeurIPS 2020 Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell

NeurIPS 2020 Robust Density Estimation Under Besov IPM Losses Ananya Uppal, Shashank Singh, Barnabas Poczos

JMLR 2020 Tuning Hyperparameters Without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

ICML 2020 VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing Zoltán Milacski, Barnabas Poczos, Andras Lorincz

UAI 2019 A Flexible Framework for Multi-Objective Bayesian Optimization Using Random Scalarizations Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

AAAI 2019 Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos

ICLR 2019 Gradient Descent Provably Optimizes Over-Parameterized Neural Networks Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh

NeurIPS 2019 Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels Simon S Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu

AISTATS 2019 Implicit Kernel Learning Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabas Poczos

ICLR 2019 Kernel Change-Point Detection with Auxiliary Deep Generative Models Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

NeurIPS 2019 Learning Local Search Heuristics for Boolean Satisfiability Emre Yolcu, Barnabas Poczos

JAIR 2019 Multi-Fidelity Gaussian Process Bandit Optimisation Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

ICML 2019 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

NeurIPS 2019 Nonparametric Density Estimation & Convergence Rates for GANs Under Besov IPM Losses Ananya Uppal, Shashank Singh, Barnabas Poczos

ICLRW 2019 Point Cloud GAN Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov

AISTATS 2019 Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent Yifan Wu, Barnabas Poczos, Aarti Singh

AISTATS 2018 A Generic Approach for Escaping Saddle Points Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola

ICML 2018 Gradient Descent Learns One-Hidden-Layer CNN: Don’t Be Afraid of Spurious Local Minima Simon Du, Jason Lee, Yuandong Tian, Aarti Singh, Barnabas Poczos

AISTATS 2018 Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning Shashank Singh, Barnabás Póczos, Jian Ma

NeurIPS 2018 Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing

NeurIPS 2018 Nonparametric Density Estimation Under Adversarial Losses Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabas Poczos

AISTATS 2018 Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

ICML 2018 Transformation Autoregressive Networks Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider

IJCAI 2017 Data-Driven Random Fourier Features Using Stein Effect Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

ICLR 2017 Deep Learning with Sets and Point Clouds Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

NeurIPS 2017 Deep Sets Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander J Smola

AAAI 2017 Enabling Dark Energy Science with Deep Generative Models of Galaxy Images Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos

ICML 2017 Equivariance Through Parameter-Sharing Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos

NeurIPS 2017 Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S Du, Chi Jin, Jason Lee, Michael I Jordan, Aarti Singh, Barnabas Poczos

NeurIPS 2017 Hypothesis Transfer Learning via Transformation Functions Simon S Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos

NeurIPS 2017 MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabas Poczos

ICML 2017 Multi-Fidelity Bayesian Optimisation with Continuous Approximations Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

UAI 2017 Near-Orthogonality Regularization in Kernel Methods Pengtao Xie, Barnabás Póczos, Eric P. Xing

ICML 2017 Nonparanormal Information Estimation Shashank Singh, Barnabás Póczos

ICCV 2017 One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

ICML 2017 The Statistical Recurrent Unit Junier B. Oliva, Barnabás Póczos, Jeff Schneider

AISTATS 2016 Bayesian Nonparametric Kernel-Learning Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

ICML 2016 Boolean Matrix Factorization and Noisy Completion via Message Passing Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner

NeurIPS 2016 Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S Du, Barnabas Poczos

ICML 2016 Estimating Cosmological Parameters from the Dark Matter Distribution Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

NeurIPS 2016 Finite-Sample Analysis of Fixed-K Nearest Neighbor Density Functional Estimators Shashank Singh, Barnabas Poczos

NeurIPS 2016 Gaussian Process Bandit Optimisation with Multi-Fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabas Poczos

AISTATS 2016 High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider

JMLR 2016 Learning Theory for Distribution Regression Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton

AAAI 2016 Linear-Time Learning on Distributions with Approximate Kernel Embeddings Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider

IJCAI 2016 Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J Smola

AISTATS 2016 Stochastic Neural Networks with Monotonic Activation Functions Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner

ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola

NeurIPS 2016 The Multi-Fidelity Multi-Armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider

UAI 2016 Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods Chun-Liang Li, Barnabás Póczos

NeurIPS 2016 Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, Eric P Xing

IJCAI 2015 Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

UAI 2015 Communication Efficient Coresets for Empirical Loss Minimization Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola

AAAI 2015 Doubly Robust Covariate Shift Correction Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola

AISTATS 2015 Fast Function to Function Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

ICML 2015 High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

NeurIPS 2015 Nonparametric Von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M Robins

AISTATS 2015 On Estimating L22 Divergence Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

NeurIPS 2015 On Variance Reduction in Stochastic Gradient Descent and Its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J Smola

AAAI 2015 On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman

AISTATS 2015 On the High Dimensional Power of a Linear-Time Two Sample Test Under Mean-Shift Alternatives Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman

AISTATS 2015 Two-Stage Sampled Learning Theory on Distributions Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur

AISTATS 2014 An Analysis of Active Learning with Uniform Feature Noise Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman

NeurIPS 2014 Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos

AISTATS 2014 Fast Distribution to Real Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

AISTATS 2014 FuSSO: Functional Shrinkage and Selection Operator Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng

ICML 2014 Generalized Exponential Concentration Inequality for Renyi Divergence Estimation Shashank Singh, Barnabas Poczos

ICML 2014 Nonparametric Estimation of Renyi Divergence and Friends Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

UAI 2014 k-NN Regression on Functional Data with Incomplete Observations Sashank J. Reddi, Barnabás Póczos

ICML 2013 Distribution to Distribution Regression Junier Oliva, Barnabas Poczos, Jeff Schneider

AISTATS 2013 Distribution-Free Distribution Regression Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman

ICML 2013 Scale Invariant Conditional Dependence Measures Sashank J Reddi, Barnabas Poczos

ICML 2012 Copula-Based Kernel Dependency Measures Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider

AISTATS 2012 Nonparametric Estimation of Conditional Information and Divergences Barnabas Poczos, Jeff Schneider

CVPR 2012 Nonparametric Kernel Estimators for Image Classification Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider

NeurIPS 2011 Group Anomaly Detection Using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff G. Schneider

AISTATS 2011 Hierarchical Probabilistic Models for Group Anomaly Detection Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas

UAI 2011 Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions Barnabás Póczos, Liang Xiong, Jeff G. Schneider

AISTATS 2011 On the Estimation of $\alpha$-Divergences Barnabas Poczos, Jeff Schneider

CVPR 2011 Online Group-Structured Dictionary Learning Zoltán Szabó, Barnabás Póczos, András Lörincz

AAAI 2010 A Cross-Entropy Method That Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner

ICML 2010 Budgeted Distribution Learning of Belief Net Parameters Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner

NeurIPS 2010 Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs Dávid Pál, Barnabás Póczos, Csaba Szepesvári

AISTATS 2010 REGO: Rank-Based Estimation of Renyi Information Using Euclidean Graph Optimization Barnabas Poczos, Sergey Kirshner, Csaba Szepesvári

JMLR 2009 Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques Barnabás Póczos, András Loőrincz

ICML 2009 Learning When to Stop Thinking and Do Something! Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant

ICML 2008 ICA and ISA Using Schweizer-Wolff Measure of Dependence Sergey Kirshner, Barnabás Póczos

JMLR 2007 Undercomplete Blind Subspace Deconvolution Zoltán Szabó, Barnabás Póczos, András Lőrincz

ICML 2005 Independent Subspace Analysis Using Geodesic Spanning Trees Barnabás Póczos, András Lörincz

ECML-PKDD 2005 Independent Subspace Analysis on Innovations Barnabás Póczos, Bálint Takács, András Lörincz