George Tucker - RankMe

82 papers A* 21A 3Misc 1Journal 44Unranked 13

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Training Language Models to Self-Correct via Reinforcement Learning. ICLR Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust
2024	J	`jnl`	Guided Evolution with Binary Discriminators for ML Program Search. CoRR John D. Co-Reyes, Yingjie Miao, George Tucker, Aleksandra Faust, Esteban Real
2024	J	`jnl`	Training Language Models to Self-Correct via Reinforcement Learning. CoRR Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust
2023	A	`conf`	Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. IROS Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine
2023	A*	`conf`	Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
2023	A*	`conf`	Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. NeurIPS Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp
2023	J	`jnl`	Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. CoRR Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp
2022	A*	`conf`	DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
2022	J	`jnl`	Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. CoRR Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Becca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine
2022	A*	`conf`	Model Selection in Batch Policy Optimization. ICML Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai
2022	A	`conf`	Offline Policy Selection under Uncertainty. AISTATS Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans
2022	J	`jnl`	Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
2022	A*	`conf`	Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
2022	J	`jnl`	Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
2021	A*	`conf`	Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. ICLR Michael R. Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi
2021	J	`jnl`	Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. CoRR Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi
2021	A*	`conf`	Benchmarks for Deep Off-Policy Evaluation. ICLR Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine
2021	J	`jnl`	Benchmarks for Deep Off-Policy Evaluation. CoRR Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine
2021	A*	`conf`	Coupled Gradient Estimators for Discrete Latent Variables. NeurIPS Zhe Dong, Andriy Mnih, George Tucker
2021	J	`jnl`	Coupled Gradient Estimators for Discrete Latent Variables. CoRR Zhe Dong, Andriy Mnih, George Tucker
2021	J	`jnl`	DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
2021	J	`jnl`	Model Selection in Batch Policy Optimization. CoRR Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai
2020	A*	`conf`	Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
2020	J	`jnl`	Conservative Q-Learning for Offline Reinforcement Learning. CoRR Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
2020	J	`jnl`	D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine
2020	A*	`conf`	DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. NeurIPS Zhe Dong, Andriy Mnih, George Tucker
2020	J	`jnl`	DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. CoRR Zhe Dong, Andriy Mnih, George Tucker
2020	A*	`conf`	Meta-Learning without Memorization. ICLR Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
2020	A*	`conf`	Model Based Reinforcement Learning for Atari. ICLR Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski
2020	J	`jnl`	Offline Policy Selection under Uncertainty. CoRR Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans
2020	J	`jnl`	Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR Sergey Levine, Aviral Kumar, George Tucker, Justin Fu
2020	J	`jnl`	RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas
2019	J	`jnl`	Behavior Regularized Offline Reinforcement Learning. CoRR Yifan Wu, George Tucker, Ofir Nachum
2019	A*	`conf`	Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. NeurIPS James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2019	J	`jnl`	Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. CoRR James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2019	—	`conf`	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
2019	A*	`conf`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	J	`jnl`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	A*	`conf`	Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
2019	—	`conf`	Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
2019	J	`jnl`	Meta-Learning without Memorization. CoRR Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
2019	J	`jnl`	Model-Based Reinforcement Learning for Atari. CoRR Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Ryan Sepassi, George Tucker, Henryk Michalewski
2019	A*	`conf`	On Variational Bounds of Mutual Information. ICML Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker
2019	J	`jnl`	On Variational Bounds of Mutual Information. CoRR Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker
2019	J	`jnl`	Reinforcement Learning Driven Heuristic Optimization. CoRR Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei
2019	—	`conf`	Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	A*	`conf`	Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. NeurIPS Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine
2019	J	`jnl`	Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. CoRR Aviral Kumar, Justin Fu, George Tucker, Sergey Levine
2019	—	`conf`	The Laplacian in RL: Learning Representations with Efficient Approximations. ICLR (Poster) Yifan Wu, George Tucker, Ofir Nachum
2019	—	`conf`	Understanding Posterior Collapse in Generative Latent Variable Models. DGS@ICLR James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2018	—	`conf`	Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. ICLR (Poster) Carlos Riquelme, George Tucker, Jasper Snoek
2018	J	`jnl`	Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. CoRR Carlos Riquelme, George Tucker, Jasper Snoek
2018	J	`jnl`	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
2018	J	`jnl`	Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein
2018	Misc	`conf`	Learning Hard Alignments with Variational Inference. ICASSP Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2018	J	`jnl`	Learning to Walk via Deep Reinforcement Learning. CoRR Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine
2018	A*	`conf`	Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. NeurIPS Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
2018	J	`jnl`	Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. CoRR Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
2018	A*	`conf`	Smoothed Action Value Functions for Learning Gaussian Policies. ICML Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
2018	J	`jnl`	Smoothed Action Value Functions for Learning Gaussian Policies. CoRR Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
2018	J	`jnl`	Soft Actor-Critic Algorithms and Applications. CoRR Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
2018	J	`jnl`	The Laplacian in RL: Learning Representations with Efficient Approximations. CoRR Yifan Wu, George Tucker, Ofir Nachum
2018	—	`conf`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2018	A*	`conf`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2018	J	`jnl`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2017	J	`jnl`	An online sequence-to-sequence model for noisy speech recognition. CoRR Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
2017	—	`conf`	Filtering Variational Objectives. NIPS Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Filtering Variational Objectives. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Learning Hard Alignments with Variational Inference. CoRR Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2017	J	`jnl`	Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting. CoRR Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni
2017	—	`conf`	Particle Value Functions. ICLR (Workshop) Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	J	`jnl`	Particle Value Functions. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	—	`conf`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
2017	—	`conf`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
2017	J	`jnl`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
2017	—	`conf`	Regularizing Neural Networks by Penalizing Confident Output Distributions. ICLR (Workshop) Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton
2017	J	`jnl`	Regularizing Neural Networks by Penalizing Confident Output Distributions. CoRR Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton
2016	J	`jnl`	Compacting Neural Network Classifiers via Dropout Training. CoRR Yotaro Kubo, George Tucker, Simon Wiesler
2016	—	`conf`	Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting. SLT Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni
2016	A	`conf`	Model Compression Applied to Small-Footprint Keyword Spotting. INTERSPEECH George Tucker, Minhua Wu, Ming Sun, Sankaran Panchapagesan, Gengshen Fu, Shiv Vitaladevuni
2014	J	`jnl`	Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. BMC Syst. Biol. Pablo Meyer, Thomas Cokelaer, Deepak Chandran, Kyung Hyuk Kim, Po-Ru Loh, George Tucker, Mark Lipson, Bonnie Berger, Clemens Kreutz, Andreas Raue, Bernhard Steiert, Jens Timmer, Erhan Bilal, Herbert M. Sauro, Gustavo Stolovitzky, Julio Saez-Rodriguez
2013	J	`jnl`	A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis. BMC Bioinform. George Tucker, Po-Ru Loh, Bonnie Berger