Barnabás Póczos

259 papers A* 48A 35B 3Journal 135Unranked 38

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation. CoRR Adrita Das, Peiran Jiang, Dantong Zhu, Barnabás Póczos, Jose Lugo-Martinez
2025	J	`jnl`	AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration. CoRR Peng Qiu, Hanqi Feng, Barnabás Póczos
2025	A*	`conf`	Chemistry-Inspired Diffusion with Non-Differentiable Guidance. ICLR Yuchen Shen, Chenhao Zhang, Sijie Fu, Chenghui Zhou, Newell Washburn, Barnabás Póczos
2025	A*	`conf`	Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention. ICLR Tongzhou Liao, Barnabás Póczos
2025	J	`jnl`	Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization. CoRR Hanqi Feng, Peng Qiu, Mengchun Zhang, Yiran Tao, You Fan, Jingtao Xu, Barnabás Póczos
2025	J	`jnl`	Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design. CoRR Amira Alakhdar, Barnabás Póczos, Newell Washburn
2025	J	`jnl`	Recovering time-varying networks from single-cell data. Bioinform. Euxhen Hasanaj, Barnabás Póczos, Ziv Bar-Joseph
2024	J	`jnl`	Chemistry-Inspired Diffusion with Non-Differentiable Guidance. CoRR Yuchen Shen, Chenhao Zhang, Sijie Fu, Chenghui Zhou, Newell Washburn, Barnabás Póczos
2024	J	`jnl`	Controllable Text Generation in the Instruction-Tuning Era. CoRR Dhananjay Ashok, Barnabás Póczos
2024	J	`jnl`	Diffusion Models in De Novo Drug Design. CoRR Amira Alakhdar, Barnabás Póczos, Newell Washburn
2024	J	`jnl`	Diffusion Models in De Novo Drug Design. J. Chem. Inf. Model. Amira Alakhdar, Barnabás Póczos, Newell Washburn
2024	J	`jnl`	Graph Attention with Random Rewiring. CoRR Tongzhou Liao, Barnabás Póczos
2024	J	`jnl`	GraphBPE: Molecular Graphs Meet Byte-Pair Encoding. CoRR Yuchen Shen, Barnabás Póczos
2024	J	`jnl`	Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling. CoRR Alan Hsu, Matthew Ho, Joyce Lin, Carleen Markey, Michelle Ntampaka, Hy Trac, Barnabás Póczos
2024	J	`jnl`	Recovering Time-Varying Networks From Single-Cell Data. CoRR Euxhen Hasanaj, Barnabás Póczos, Ziv Bar-Joseph
2023	J	`jnl`	Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE. CoRR Chenghui Zhou, Barnabás Póczos
2023	J	`jnl`	Task-Based MoE for Multitask Multilingual Machine Translation. CoRR Hai Pham, Young Jin Kim, Subhabrata Mukherjee, David P. Woodruff, Barnabás Póczos, Hany Hassan Awadalla
2023	J	`jnl`	The student becomes the master: Matching GPT3 on Scientific Factual Error Correction. CoRR Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabás Póczos
2023	—	`conf`	The student becomes the master: Outperforming GPT3 on Scientific Factual Error Correction. EMNLP (Findings) Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabás Póczos
2022	J	`jnl`	Improving Molecule Properties Through 2-Stage VAE. CoRR Chenghui Zhou, Barnabás Póczos
2022	J	`jnl`	On the Algorithmic Stability and Generalization of Adaptive Optimization Methods. CoRR Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos
2021	J	`jnl`	Coarse-to-Fine Curriculum Learning. CoRR Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos
2021	A*	`conf`	Re-TACRED: Addressing Shortcomings of the TACRED Dataset. AAAI George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos
2021	J	`jnl`	Re-TACRED: Addressing Shortcomings of the TACRED Dataset. CoRR George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos
2021	—	`conf`	StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer. NAACL-HLT Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard H. Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
2021	J	`jnl`	StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer. CoRR Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard H. Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
2021	A	`conf`	Unsupervised program synthesis for images by sampling without replacement. UAI Chenghui Zhou, Chun-Liang Li, Barnabás Póczos
2020	J	`jnl`	Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets. CoRR Ilqar Ramazanli, Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos
2020	J	`jnl`	Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning. CoRR Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan
2020	A	`conf`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2020	A*	`conf`	Contextual Parameter Generation for Knowledge Graph Link Prediction. AAAI George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos
2020	J	`jnl`	Covariate Distribution Aware Meta-learning. CoRR Amrith Setlur, Saket Dingliwal, Barnabás Póczos
2020	—	`conf`	Efficient Meta Lifelong-Learning with Limited Memory. EMNLP (1) Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell
2020	J	`jnl`	Efficient Meta Lifelong-Learning with Limited Memory. CoRR Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell
2020	J	`jnl`	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. Comput. Softw. Big Sci. Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos
2020	J	`jnl`	Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction. CoRR George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos
2020	A*	`conf`	Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020	J	`jnl`	Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020	A*	`conf`	Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. NeurIPS Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell
2020	J	`jnl`	Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. CoRR Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell
2020	A	`conf`	Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. INTERSPEECH Amrith Setlur, Barnabás Póczos, Alan W. Black
2020	J	`jnl`	Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. CoRR Amrith Setlur, Barnabás Póczos, Alan W. Black
2020	J	`jnl`	Optimal Adaptive Matrix Completion. CoRR Ilqar Ramazanli, Barnabás Póczos
2020	A*	`conf`	Politeness Transfer: A Tag and Generate Approach. ACL Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
2020	J	`jnl`	Politeness Transfer: A Tag and Generate Approach. CoRR Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
2020	A*	`conf`	Robust Density Estimation under Besov IPM Losses. NeurIPS Ananya Uppal, Shashank Singh, Barnabás Póczos
2020	J	`jnl`	Robust Density Estimation under Besov IPM Losses. CoRR Ananya Uppal, Shashank Singh, Barnabás Póczos
2020	B	`conf`	Robust Handwriting Recognition with Limited and Noisy Data. ICFHR Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
2020	J	`jnl`	Robust Handwriting Recognition with Limited and Noisy Data. CoRR Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
2020	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2020	J	`jnl`	Unsupervised Program Synthesis for Images using Tree-Structured LSTM. CoRR Chenghui Zhou, Chun-Liang Li, Barnabás Póczos
2020	A*	`conf`	VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing. ICML Zoltán Ádám Milacski, Barnabás Póczos, András Lörincz
2019	J	`jnl`	A Deep Reinforcement Learning Approach for Global Routing. CoRR Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabás Póczos, Kenji Shimada, Levent Burak Kara
2019	A	`conf`	A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. UAI Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
2019	J	`jnl`	Better Approximate Inference for Partial Likelihood Models with a Latent Structure. CoRR Amrith Setlur, Barnabás Póczos
2019	A*	`conf`	Characterizing and Avoiding Negative Transfer. CVPR Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell
2019	J	`jnl`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	—	`conf`	Competence-based Curriculum Learning for Neural Machine Translation. NAACL-HLT (1) Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, Tom M. Mitchell
2019	J	`jnl`	Competence-based Curriculum Learning for Neural Machine Translation. CoRR Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, Tom M. Mitchell
2019	J	`jnl`	Developing Creative AI to Generate Sculptural Objects. CoRR Songwei Ge, Austin Dill, Eunsu Kang, Chun-Liang Li, Lingyao Zhang, Manzil Zaheer, Barnabás Póczos
2019	A	`conf`	Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet. BMVC Zoltán Ádám Milacski, Barnabás Póczos, András Lorincz
2019	J	`jnl`	End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. CoRR Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabás Póczos, Emanuele Usai
2019	A*	`conf`	Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities. AAAI Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos
2019	—	`conf`	Gradient Descent Provably Optimizes Over-parameterized Neural Networks. ICLR (Poster) Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh
2019	A*	`conf`	Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, Keyulu Xu
2019	J	`jnl`	Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. CoRR Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
2019	B	`conf`	Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification. IJCNN Zoltán Ádám Milacski, Barnabás Póczos, András Lörincz
2019	A	`conf`	Implicit Kernel Learning. AISTATS Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos
2019	J	`jnl`	Implicit Kernel Learning. CoRR Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos
2019	—	`conf`	Kernel Change-point Detection with Auxiliary Deep Generative Models. ICLR (Poster) Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos
2019	J	`jnl`	Kernel Change-point Detection with Auxiliary Deep Generative Models. CoRR Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos
2019	A*	`conf`	LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds. CVPR Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh
2019	J	`jnl`	LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds. CoRR Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh
2019	J	`jnl`	Learned Interpolation for 3D Generation. CoRR Austin Dill, Songwei Ge, Eunsu Kang, Chun-Liang Li, Barnabás Póczos
2019	A*	`conf`	Learning Local Search Heuristics for Boolean Satisfiability. NeurIPS Emre Yolcu, Barnabás Póczos
2019	J	`jnl`	LucidDream: Controlled Temporally-Consistent DeepDream on Videos. CoRR Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos
2019	J	`jnl`	Multi-fidelity Gaussian Process Bandit Optimisation. J. Artif. Intell. Res. Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2019	A*	`conf`	Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2019	A*	`conf`	Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses. NeurIPS Ananya Uppal, Shashank Singh, Barnabás Póczos
2019	J	`jnl`	Nonparametric Density Estimation under Besov IPM Losses. CoRR Ananya Uppal, Shashank Singh, Barnabás Póczos
2019	—	`conf`	Point Cloud GAN. DGS@ICLR Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov
2019	J	`jnl`	Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. Quant. Biol. Shashank Singh, Yang Yang, Barnabás Póczos, Jian Ma
2019	J	`jnl`	ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	RotationOut as a Regularization Method for Neural Network. CoRR Kai Hu, Barnabás Póczos
2019	A	`conf`	Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. AISTATS Yifan Wu, Barnabás Póczos, Aarti Singh
2019	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. CoRR Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
2018	A	`conf`	A Generic Approach for Escaping Saddle points. AISTATS Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola
2018	J	`jnl`	Cautious Deep Learning. CoRR Yotam Hechtlinger, Barnabás Póczos, Larry A. Wasserman
2018	J	`jnl`	Characterizing and Avoiding Negative Transfer. CoRR Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell
2018	A	`conf`	Classifier Two Sample Test for Video Anomaly Detections. BMVC Yusha Liu, Chun-Liang Li, Barnabás Póczos
2018	J	`jnl`	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. CoRR Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos
2018	J	`jnl`	Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities. CoRR Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos
2018	A*	`conf`	Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. ICML Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos
2018	J	`jnl`	Gradient Descent Provably Optimizes Over-parameterized Neural Networks. CoRR Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh
2018	J	`jnl`	Hallucinating Point Cloud into 3D Sculptural Object. CoRR Chun-Liang Li, Eunsu Kang, Songwei Ge, Lingyao Zhang, Austin Dill, Manzil Zaheer, Barnabás Póczos
2018	J	`jnl`	Learning to Predict the Cosmological Structure Formation. CoRR Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
2018	J	`jnl`	Minimax Distribution Estimation in Wasserstein Distance. CoRR Shashank Singh, Barnabás Póczos
2018	J	`jnl`	Minimax Estimation of Quadratic Fourier Functionals. CoRR Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos
2018	A	`conf`	Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning. AISTATS Shashank Singh, Barnabás Póczos, Jian Ma
2018	J	`jnl`	Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	A*	`conf`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	A*	`conf`	Nonparametric Density Estimation under Adversarial Losses. NeurIPS Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos
2018	J	`jnl`	Nonparametric Density Estimation under Adversarial Losses. CoRR Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos
2018	A	`conf`	Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	J	`jnl`	Point Cloud GAN. CoRR Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov
2018	A	`conf`	Robust Plant Phenotyping via Model-Based Optimization. IROS Paloma Sodhi, Hanqi Sun, Barnabás Póczos, David Wettergreen
2018	J	`jnl`	Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis. CoRR Hai Pham, Thomas Manzini, Paul Pu Liang, Barnabás Póczos
2018	—	`conf`	Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. MICCAI (1) Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich
2018	J	`jnl`	Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. CoRR Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich
2018	J	`jnl`	Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. CoRR Yifan Wu, Barnabás Póczos, Aarti Singh
2018	A*	`conf`	Transformation Autoregressive Networks. ICML Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider
2017	J	`jnl`	A Generic Approach for Escaping Saddle points. CoRR Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola
2017	J	`jnl`	Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos
2017	A	`conf`	BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals. SDM Xiao Fu, Kejun Huang, Otilia Stretcu, Hyun Ah Song, Evangelos E. Papalexakis, Partha P. Talukdar, Tom M. Mitchell, Nicholas D. Sidiropoulos, Christos Faloutsos, Barnabás Póczos
2017	A*	`conf`	Data-driven Random Fourier Features using Stein Effect. IJCAI Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos
2017	J	`jnl`	Data-driven Random Fourier Features using Stein Effect. CoRR Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos
2017	—	`conf`	Deep Learning with Sets and Point Clouds. ICLR (Workshop) Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
2017	—	`conf`	Deep Sets. NIPS Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola
2017	J	`jnl`	Deep Sets. CoRR Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola
2017	A*	`conf`	Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
2017	A*	`conf`	Equivariance Through Parameter-Sharing. ICML Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
2017	J	`jnl`	Equivariance Through Parameter-Sharing. CoRR Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
2017	J	`jnl`	Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne C. Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos
2017	—	`conf`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos
2017	J	`jnl`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh
2017	J	`jnl`	Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. CoRR Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabás Póczos, Aarti Singh
2017	—	`conf`	High-Throughput Robotic Phenotyping of Energy Sorghum Crops. FSR Srinivasan Vijayarangan, Paloma Sodhi, Prathamesh Kini, James Bourne, Simon S. Du, Hanqi Sun, Barnabás Póczos, Dimitrios Apostolopoulos, David Wettergreen
2017	—	`conf`	Hypothesis Transfer Learning via Transformation Functions. NIPS Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos
2017	—	`conf`	MMD GAN: Towards Deeper Understanding of Moment Matching Network. NIPS Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos
2017	J	`jnl`	MMD GAN: Towards Deeper Understanding of Moment Matching Network. CoRR Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos
2017	A*	`conf`	Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos
2017	A	`conf`	Near-Orthogonality Regularization in Kernel Methods. UAI Pengtao Xie, Barnabás Póczos, Eric P. Xing
2017	A*	`conf`	Nonparanormal Information Estimation. ICML Shashank Singh, Barnabás Póczos
2017	J	`jnl`	Nonparanormal Information Estimation. CoRR Shashank Singh, Barnabás Póczos
2017	J	`jnl`	On the Reconstruction Risk of Convolutional Sparse Dictionary Learning. CoRR Shashank Singh, Barnabás Póczos, Jian Ma
2017	A*	`conf`	One Network to Solve Them All - Solving Linear Inverse Problems Using Deep Projection Models. ICCV Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar
2017	J	`jnl`	One Network to Solve Them All - Solving Linear Inverse Problems using Deep Projection Models. CoRR Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan
2017	J	`jnl`	Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artif. Intell. Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2017	J	`jnl`	Recurrent Estimation of Distributions. CoRR Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider
2017	A*	`conf`	The Statistical Recurrent Unit. ICML Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2017	J	`jnl`	The Statistical Recurrent Unit. CoRR Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2016	J	`jnl`	AIDE: Fast and Communication Efficient Distributed Optimization. CoRR Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation. CoRR Shashank Singh, Barnabás Póczos
2016	J	`jnl`	Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. CoRR Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos
2016	A	`conf`	Bayesian Nonparametric Kernel-Learning. AISTATS Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2016	A*	`conf`	Boolean Matrix Factorization and Noisy Completion via Message Passing. ICML Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner
2016	J	`jnl`	Deep Learning with Sets and Point Clouds. CoRR Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
2016	—	`conf`	Efficient Nonparametric Smoothness Estimation. NIPS Shashank Singh, Simon S. Du, Barnabás Póczos
2016	J	`jnl`	Efficient Nonparametric Smoothness Estimation. CoRR Shashank Singh, Simon S. Du, Barnabás Póczos
2016	J	`jnl`	Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
2016	A*	`conf`	Estimating Cosmological Parameters from the Dark Matter Distribution. ICML Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos
2016	J	`jnl`	Exponential Concentration of a Density Functional Estimator. CoRR Shashank Singh, Barnabás Póczos
2016	J	`jnl`	Fast Incremental Method for Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Fast incremental method for smooth nonconvex optimization. CDC Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators. NIPS Shashank Singh, Barnabás Póczos
2016	J	`jnl`	Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators. CoRR Shashank Singh, Barnabás Póczos
2016	—	`conf`	Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2016	J	`jnl`	Generalized Exponential Concentration Inequality for Rényi Divergence Estimation. CoRR Shashank Singh, Barnabás Póczos
2016	A	`conf`	High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider
2016	J	`jnl`	Learning Theory for Distribution Regression. J. Mach. Learn. Res. Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton
2016	A*	`conf`	Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2016	J	`jnl`	Multi-fidelity Gaussian Process Bandit Optimisation. CoRR Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2016	A*	`conf`	Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. IJCAI Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2016	A	`conf`	Nonparametric distribution regression applied to sensor modeling. IROS Abhijeet Tallavajhula, Barnabás Póczos, Alonzo Kelly
2016	—	`conf`	Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput. Biol. Fang-Cheng Yeh, Jean M. Vettel, Aarti Singh, Barnabás Póczos, Scott T. Grafton, Kirk I. Erickson, Wen-Yih Isaac Tseng, Timothy D. Verstynen
2016	J	`jnl`	Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	A	`conf`	Stochastic Neural Networks with Monotonic Activation Functions. AISTATS Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner
2016	J	`jnl`	Stochastic Neural Networks with Monotonic Activation Functions. CoRR Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner
2016	A*	`conf`	Stochastic Variance Reduction for Nonconvex Optimization. ICML Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Stochastic Variance Reduction for Nonconvex Optimization. CoRR Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	The Multi-fidelity Multi-armed Bandit. NIPS Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider
2016	J	`jnl`	The Multi-fidelity Multi-armed Bandit. CoRR Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos
2016	J	`jnl`	Transformation Function Based Methods for Model Shift. CoRR Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos
2016	A	`conf`	Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. UAI Chun-Liang Li, Barnabás Póczos
2016	—	`conf`	Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing
2015	J	`jnl`	Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	J	`jnl`	An Analysis of Active Learning With Uniform Feature Noise. CoRR Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	A*	`conf`	Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	A	`conf`	Communication Efficient Coresets for Empirical Loss Minimization. UAI Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	Deep Mean Maps. CoRR Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
2015	A*	`conf`	Doubly Robust Covariate Shift Correction. AAAI Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data. Adv. Eng. Informatics In-Soo Jung, Mario Berges, James H. Garrett Jr., Barnabás Póczos
2015	A	`conf`	Fast Function to Function Regression. AISTATS Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider
2015	A*	`conf`	High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	J	`jnl`	High Dimensional Bayesian Optimisation and Bandits via Additive Models. CoRR Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	J	`jnl`	Linear-time Learning on Distributions with Approximate Kernel Embeddings. CoRR Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2015	—	`conf`	Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
2015	A	`conf`	On Estimating L22 Divergence. AISTATS Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
2015	—	`conf`	On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2015	A*	`conf`	On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	A	`conf`	On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	A	`conf`	Two-stage sampled learning theory on distributions. AISTATS Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	A	`conf`	An Analysis of Active Learning with Uniform Feature Noise. AISTATS Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	J	`jnl`	Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. CoRR Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	—	`conf`	Exponential Concentration of a Density Functional Estimator. NIPS Shashank Singh, Barnabás Póczos
2014	A	`conf`	Fast Distribution To Real Regression. AISTATS Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2014	J	`jnl`	Fast Function to Function Regression. CoRR Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider
2014	A	`conf`	FuSSO: Functional Shrinkage and Selection Operator. AISTATS Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng
2014	A*	`conf`	Generalized Exponential Concentration Inequality for Renyi Divergence Estimation. ICML Shashank Singh, Barnabás Póczos
2014	J	`jnl`	Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
2014	J	`jnl`	Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	J	`jnl`	Learning Theory for Distribution Regression. CoRR Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	A*	`conf`	Nonparametric Estimation of Renyi Divergence and Friends. ICML Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
2014	J	`jnl`	On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	A	`conf`	k-NN Regression on Functional Data with Incomplete Observations. UAI Sashank J. Reddi, Barnabás Póczos
2013	A*	`conf`	Active learning and search on low-rank matrices. KDD Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
2013	—	`conf`	Distribution to Distribution Regression. ICML (3) Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2013	J	`jnl`	Distribution-Free Distribution Regression CoRR Barnabás Póczos, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
2013	A	`conf`	Distribution-Free Distribution Regression. AISTATS Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman
2013	A*	`conf`	Efficient Learning on Point Sets. ICDM Liang Xiong, Barnabás Póczos, Jeff G. Schneider
2013	J	`jnl`	Fast Distribution To Real Regression. CoRR Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2013	J	`jnl`	FuSSO: Functional Shrinkage and Selection Operator. CoRR Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng
2013	—	`conf`	Scale Invariant Conditional Dependence Measures. ICML (3) Sashank J. Reddi, Barnabás Póczos
2012	J	`jnl`	Collaborative Filtering via Group-Structured Dictionary Learning CoRR Zoltán Szabó, Barnabás Póczos, András Lörincz
2012	—	`conf`	Collaborative Filtering via Group-Structured Dictionary Learning. LVA/ICA Zoltán Szabó, Barnabás Póczos, András Lörincz
2012	J	`jnl`	Copula-based Kernel Dependency Measures CoRR Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider
2012	A*	`conf`	Copula-based Kernel Dependency Measures. ICML Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider
2012	J	`jnl`	Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions CoRR Barnabás Póczos, Liang Xiong, Jeff G. Schneider
2012	A	`conf`	Nonparametric Estimation of Conditional Information and Divergences. AISTATS Barnabás Póczos, Jeff G. Schneider
2012	A*	`conf`	Nonparametric kernel estimators for image classification. CVPR Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
2012	J	`jnl`	Separation theorem for independent subspace analysis and its consequences. Pattern Recognit. Zoltán Szabó, Barnabás Póczos, András Lörincz
2012	J	`jnl`	Support Distribution Machines CoRR Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
2011	—	`conf`	Group Anomaly Detection using Flexible Genre Models. NIPS Liang Xiong, Barnabás Póczos, Jeff G. Schneider
2011	A	`conf`	Hierarchical Probabilistic Models for Group Anomaly Detection. AISTATS Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas
2011	A	`conf`	Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. UAI Barnabás Póczos, Liang Xiong, Jeff G. Schneider
2011	—	`conf`	Nonparametric divergence estimators for independent subspace analysis. EUSIPCO Barnabás Póczos, Zoltán Szabó, Jeff G. Schneider
2011	—	`conf`	Nonparametric independent process analysis. EUSIPCO Zoltán Szabó, Barnabás Póczos
2011	A	`conf`	On the Estimation of alpha-Divergences. AISTATS Barnabás Póczos, Jeff G. Schneider
2011	A*	`conf`	Online group-structured dictionary learning. CVPR Zoltán Szabó, Barnabás Póczos, András Lörincz
2010	A*	`conf`	A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra. AAAI Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner
2010	J	`jnl`	Auto-regressive independent process analysis without combinatorial efforts. Pattern Anal. Appl. Zoltán Szabó, Barnabás Póczos, András Lörincz
2010	A*	`conf`	Budgeted Distribution Learning of Belief Net Parameters. ICML Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner
2010	—	`conf`	Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. NIPS Dávid Pál, Barnabás Póczos, Csaba Szepesvári
2010	J	`jnl`	Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs CoRR Dávid Pál, Barnabás Póczos, Csaba Szepesvári
2010	A	`conf`	REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization. AISTATS Barnabás Póczos, Sergey Kirshner, Csaba Szepesvári
2009	J	`jnl`	Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques. J. Mach. Learn. Res. Barnabás Póczos, András Lörincz
2009	A*	`conf`	Learning when to stop thinking and do something! ICML Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant
2008	J	`jnl`	D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks CoRR Barnabás Póczos, András Lörincz
2008	A*	`conf`	ICA and ISA using Schweizer-Wolff measure of dependence. ICML Sergey Kirshner, Barnabás Póczos
2007	—	`conf`	Independent Process Analysis Without a Priori Dimensional Information. ICA Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, András Lörincz
2007	—	`conf`	Post Nonlinear Independent Subspace Analysis. ICANN (1) Zoltán Szabó, Barnabás Póczos, Gábor Szirtes, András Lörincz
2007	—	`conf`	Undercomplete Blind Subspace Deconvolution Via Linear Prediction. ECML Zoltán Szabó, Barnabás Póczos, András Lörincz
2007	J	`jnl`	Undercomplete Blind Subspace Deconvolution. J. Mach. Learn. Res. Zoltán Szabó, Barnabás Póczos, András Lörincz
2006	—	`conf`	Cross-Entropy Optimization for Independent Process Analysis. ICA Zoltán Szabó, Barnabás Póczos, András Lörincz
2006	J	`jnl`	Non-combinatorial estimation of independent autoregressive sources. Neurocomputing Barnabás Póczos, András Lörincz
2005	—	`conf`	Independent Subspace Analysis Using ICANN (2) Barnabás Póczos, András Lörincz
2005	—	`conf`	Independent Subspace Analysis on Innovations. ECML Barnabás Póczos, Bálint Takács, András Lörincz
2005	A*	`conf`	Independent subspace analysis using geodesic spanning trees. ICML Barnabás Póczos, András Lörincz
2005	J	`jnl`	Neural Kalman filter. Neurocomputing Gábor Szirtes, Barnabás Póczos, András Lörincz
2004	B	`conf`	Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool. IJCNN György Hévízi, Mihály Biczó, Barnabás Póczos, Zoltán Szabó, Bálint Takács, András Lörincz
2003	J	`jnl`	Cost Component Analysis. Int. J. Neural Syst. András Lörincz, Barnabás Póczos
2003	J	`jnl`	Kalman-filtering using local interactions CoRR Barnabás Póczos, András Lörincz
2002	A	`conf`	Non-negative matrix factorization extended by sparse code shrinkage and weight sparsification non-negative matrix factorization algorithms. ECAI Botond Szatmáry, Barnabás Póczos, Julian Eggert, Edgar Körner, András Lörincz