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2020 – today
- 2024
[i44]John D. Co-Reyes, Yingjie Miao, George Tucker, Aleksandra Faust, Esteban Real:
Guided Evolution with Binary Discriminators for ML Program Search. CoRR abs/2402.05821 (2024)- [i43]Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Cristian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, et al.:
Gemma: Open Models Based on Gemini Research and Technology. CoRR abs/2403.08295 (2024) - [i42]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:
Training Language Models to Self-Correct via Reinforcement Learning. CoRR abs/2409.12917 (2024) - 2023
- [c37]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR 2023 - [c36]Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust
, Shimon Whiteson, Dragomir Anguelov, Sergey Levine:
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. IROS 2023: 7553-7560 - [c35]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:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. NeurIPS 2023 - [i41]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:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. CoRR abs/2310.08710 (2023) - [i40]Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.:
Gemini: A Family of Highly Capable Multimodal Models. CoRR abs/2312.11805 (2023) - 2022
- [c34]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. AISTATS 2022: 4376-4396 - [c33]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR 2022 - [c32]Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. ICML 2022: 12542-12569 - [c31]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS 2022 - [i39]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR abs/2211.02016 (2022) - [i38]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR abs/2211.15144 (2022) - [i37]Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Becca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine:
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. CoRR abs/2212.11419 (2022) - 2021
- [c30]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:
Benchmarks for Deep Off-Policy Evaluation. ICLR 2021 - [c29]Michael R. Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi:
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. ICLR 2021 - [c28]Zhe Dong, Andriy Mnih, George Tucker:
Coupled Gradient Estimators for Discrete Latent Variables. NeurIPS 2021: 24498-24508 - [i36]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:
Benchmarks for Deep Off-Policy Evaluation. CoRR abs/2103.16596 (2021) - [i35]Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi:
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. CoRR abs/2104.13877 (2021) - [i34]Zhe Dong, Andriy Mnih, George Tucker:
Coupled Gradient Estimators for Discrete Latent Variables. CoRR abs/2106.08056 (2021) - [i33]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR abs/2112.04716 (2021) - [i32]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. CoRR abs/2112.12320 (2021) - 2020
- [c27]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:
Model Based Reinforcement Learning for Atari. ICLR 2020 - [c26]Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn:
Meta-Learning without Memorization. ICLR 2020 - [c25]Zhe Dong, Andriy Mnih, George Tucker:
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. NeurIPS 2020 - [c24]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS 2020 - [i31]Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine:
D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR abs/2004.07219 (2020) - [i30]Sergey Levine, Aviral Kumar, George Tucker, Justin Fu:
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR abs/2005.01643 (2020) - [i29]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. CoRR abs/2006.04779 (2020) - [i28]Zhe Dong, Andriy Mnih, George Tucker:
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. CoRR abs/2006.10680 (2020) - [i27]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:
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR abs/2006.13888 (2020) - [i26]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. CoRR abs/2012.06919 (2020)
2010 – 2019
- 2019
- [c23]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR 2019 - [c22]James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi:
Understanding Posterior Collapse in Generative Latent Variable Models. DGS@ICLR 2019 - [c21]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) 2019 - [c20]Yifan Wu, George Tucker, Ofir Nachum:
The Laplacian in RL: Learning Representations with Efficient Approximations. ICLR (Poster) 2019 - [c19]Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML 2019: 4264-4273 - [c18]Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker:
On Variational Bounds of Mutual Information. ICML 2019: 5171-5180 - [c17]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS 2019: 8499-8511 - [c16]James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi:
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. NeurIPS 2019: 9403-9413 - [c15]Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. NeurIPS 2019: 11761-11771 - [c14]Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems 2019 - [i25]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:
Model-Based Reinforcement Learning for Atari. CoRR abs/1903.00374 (2019) - [i24]Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker:
On Variational Bounds of Mutual Information. CoRR abs/1905.06922 (2019) - [i23]Aviral Kumar, Justin Fu, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. CoRR abs/1906.00949 (2019) - [i22]Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei:
Reinforcement Learning Driven Heuristic Optimization. CoRR abs/1906.06639 (2019) - [i21]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR abs/1910.14265 (2019) - [i20]James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi:
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. CoRR abs/1911.02469 (2019) - [i19]Yifan Wu, George Tucker, Ofir Nachum:
Behavior Regularized Offline Reinforcement Learning. CoRR abs/1911.11361 (2019) - [i18]Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn:
Meta-Learning without Memorization. CoRR abs/1912.03820 (2019) - 2018
- [c13]Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. ICASSP 2018: 5799-5803 - [c12]Carlos Riquelme, George Tucker, Jasper Snoek:
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. ICLR (Poster) 2018 - [c11]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) 2018 - [c10]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. ICML 2018: 3689-3697 - [c9]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML 2018: 5022-5031 - [c8]Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee:
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. NeurIPS 2018: 8234-8244 - [i17]Carlos Riquelme, George Tucker, Jasper Snoek:
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. CoRR abs/1802.09127 (2018) - [i16]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR abs/1802.10031 (2018) - [i15]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. CoRR abs/1803.02348 (2018) - [i14]Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein:
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR abs/1806.10230 (2018) - [i13]Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee:
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. CoRR abs/1807.01675 (2018) - [i12]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR abs/1810.04152 (2018) - [i11]Yifan Wu, George Tucker, Ofir Nachum:
The Laplacian in RL: Learning Representations with Efficient Approximations. CoRR abs/1810.04586 (2018) - [i10]Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic Algorithms and Applications. CoRR abs/1812.05905 (2018) - [i9]Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk via Deep Reinforcement Learning. CoRR abs/1812.11103 (2018) - 2017
- [c7]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c6]Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton:
Regularizing Neural Networks by Penalizing Confident Output Distributions. ICLR (Workshop) 2017 - [c5]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) 2017 - [c4]George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 2017: 2627-2636 - [c3]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. NIPS 2017: 6573-6583 - [i8]Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton:
Regularizing Neural Networks by Penalizing Confident Output Distributions. CoRR abs/1701.06548 (2017) - [i7]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. CoRR abs/1703.05820 (2017) - [i6]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR abs/1703.07370 (2017) - [i5]Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni:
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting. CoRR abs/1705.02411 (2017) - [i4]Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. CoRR abs/1705.05524 (2017) - [i3]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. CoRR abs/1705.09279 (2017) - [i2]Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly:
An online sequence-to-sequence model for noisy speech recognition. CoRR abs/1706.06428 (2017) - 2016
- [c2]George Tucker, Minhua Wu, Ming Sun, Sankaran Panchapagesan, Gengshen Fu, Shiv Vitaladevuni:
Model Compression Applied to Small-Footprint Keyword Spotting. INTERSPEECH 2016: 1878-1882 - [c1]Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni:
Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting. SLT 2016: 474-480 - [i1]Yotaro Kubo, George Tucker, Simon Wiesler:
Compacting Neural Network Classifiers via Dropout Training. CoRR abs/1611.06148 (2016) - 2014
- [j2]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:
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. BMC Syst. Biol. 8: 13 (2014) - 2013
- [j1]George Tucker, Po-Ru Loh, Bonnie Berger:
A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis. BMC Bioinform. 14: 299 (2013)
Coauthor Index
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