George Tucker

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 80

Top Topics

Keyword Spotting

Latent Variables

Reinforcement Learning

Top Venues

Publications

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 (2024)
John D. Co-Reyes, Yingjie Miao, George Tucker, Aleksandra Faust, Esteban Real
Guided Evolution with Binary Discriminators for ML Program Search. CoRR (2024)
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 (2023)
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 (2023)
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)
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR (2023)
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)
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR (2022)
Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS (2022)
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR (2022)
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 (2022)
Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai
Model Selection in Batch Policy Optimization. ICML (2022)
Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR (2022)
Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans
Offline Policy Selection under Uncertainty. AISTATS (2022)
Zhe Dong, Andriy Mnih, George Tucker
Coupled Gradient Estimators for Discrete Latent Variables. NeurIPS (2021)
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR (2021)
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, Thomas Paine
Benchmarks for Deep Off-Policy Evaluation. ICLR (2021)
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)
Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai
Model Selection in Batch Policy Optimization. CoRR (2021)
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 (2021)
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 (2021)
Zhe Dong, Andriy Mnih, George Tucker
Coupled Gradient Estimators for Discrete Latent Variables. CoRR (2021)
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
Meta-Learning without Memorization. ICLR (2020)
Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans
Offline Policy Selection under Uncertainty. CoRR (2020)
Sergey Levine, Aviral Kumar, George Tucker, Justin Fu
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR (2020)
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 (2020)
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS (2020)
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)
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
Conservative Q-Learning for Offline Reinforcement Learning. CoRR (2020)
Zhe Dong, Andriy Mnih, George Tucker
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. NeurIPS (2020)
Zhe Dong, Andriy Mnih, George Tucker
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. CoRR (2020)
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine
D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR (2020)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR (2019)
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems (2019)
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 (2019)
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker
On Variational Bounds of Mutual Information. CoRR (2019)
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. NeurIPS (2019)
Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei
Reinforcement Learning Driven Heuristic Optimization. CoRR (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. NeurIPS (2019)
Yifan Wu, George Tucker, Ofir Nachum
Behavior Regularized Offline Reinforcement Learning. CoRR (2019)
Aviral Kumar, Justin Fu, George Tucker, Sergey Levine
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. CoRR (2019)
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) (2019)
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
Meta-Learning without Memorization. CoRR (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Understanding Posterior Collapse in Generative Latent Variable Models. DGS@ICLR (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. CoRR (2019)
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alex Alemi, George Tucker
On Variational Bounds of Mutual Information. ICML (2019)
Yifan Wu, George Tucker, Ofir Nachum
The Laplacian in RL: Learning Representations with Efficient Approximations. ICLR (Poster) (2019)
Carlos Riquelme, George Tucker, Jasper Snoek
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. CoRR (2018)
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. NeurIPS (2018)
Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
Smoothed Action Value Functions for Learning Gaussian Policies. CoRR (2018)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR (2018)
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. CoRR (2018)
Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR (2018)
Yifan Wu, George Tucker, Ofir Nachum
The Laplacian in RL: Learning Representations with Efficient Approximations. CoRR (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR (2018)
Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine
Learning to Walk via Deep Reinforcement Learning. CoRR (2018)
Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. ICASSP (2018)
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)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML (2018)
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 (2018)
Carlos Riquelme, George Tucker, Jasper Snoek
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. ICLR (Poster) (2018)
Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
Smoothed Action Value Functions for Learning Gaussian Policies. ICML (2018)
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 (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. CoRR (2017)
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)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. CoRR (2017)
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton
Regularizing Neural Networks by Penalizing Confident Output Distributions. CoRR (2017)
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) (2017)
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. ICLR (Workshop) (2017)
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton
Regularizing Neural Networks by Penalizing Confident Output Distributions. ICLR (Workshop) (2017)
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 (2017)
Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. CoRR (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. NIPS (2017)
George Tucker, Minhua Wu, Ming Sun, Sankaran Panchapagesan, Gengshen Fu, Shiv Vitaladevuni
Model Compression Applied to Small-Footprint Keyword Spotting. INTERSPEECH (2016)
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)
Yotaro Kubo, George Tucker, Simon Wiesler
Compacting Neural Network Classifiers via Dropout Training. CoRR (2016)
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 (2014)
George Tucker, Po-Ru Loh, Bonnie Berger
A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis. BMC Bioinform. 14 (2013)