George Tucker

According to our database, George Tucker authored at least 53 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Gemma: Open Models Based on Gemini Research and Technology.

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-Christian 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.

CoRR, 2024

Guided Evolution with Binary Discriminators for ML Program Search.

John D. Co-Reyes, Yingjie Miao, George Tucker, Aleksandra Faust, Esteban Real

CoRR, 2024

2023

Gemini: A Family of Highly Capable Multimodal Models.

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.

CoRR, 2023

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios.

Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine

IROS, 2023

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes.

Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Oracle Inequalities for Model Selection in Offline Reinforcement Learning.

Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model Selection in Batch Policy Optimization.

Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai

Proceedings of the International Conference on Machine Learning, 2022

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization.

Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Offline Policy Selection under Uncertainty.

Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization.

Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

CoRR, 2021

Coupled Gradient Estimators for Discrete Latent Variables.

Zhe Dong, Andriy Mnih, George Tucker

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization.

Michael R. Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

Proceedings of the 9th International Conference on Learning Representations, 2021

Benchmarks for Deep Off-Policy Evaluation.

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

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

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

CoRR, 2020

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Sergey Levine, Aviral Kumar, George Tucker, Justin Fu

CoRR, 2020

D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine

CoRR, 2020

Conservative Q-Learning for Offline Reinforcement Learning.

Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables.

Zhe Dong, Andriy Mnih, George Tucker

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Learning without Memorization.

Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

Proceedings of the 8th International Conference on Learning Representations, 2020

Model Based Reinforcement Learning for Atari.

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Behavior Regularized Offline Reinforcement Learning.

Yifan Wu, George Tucker, Ofir Nachum

CoRR, 2019

Reinforcement Learning Driven Heuristic Optimization.

Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei

CoRR, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, George Tucker, Sergey Levine

CoRR, 2019

Model-Based Reinforcement Learning for Atari.

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

CoRR, 2019

Learning to Walk Via Deep Reinforcement Learning.

Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine

Proceedings of the Robotics: Science and Systems XV, 2019

Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse.

James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Energy-Inspired Models: Learning with Sampler-Induced Distributions.

Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Variational Bounds of Mutual Information.

Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker

Proceedings of the 36th International Conference on Machine Learning, 2019

Guided evolutionary strategies: augmenting random search with surrogate gradients.

Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein

Proceedings of the 36th International Conference on Machine Learning, 2019

The Laplacian in RL: Learning Representations with Efficient Approximations.

Yifan Wu, George Tucker, Ofir Nachum

Proceedings of the 7th International Conference on Learning Representations, 2019

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives.

George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

Proceedings of the 7th International Conference on Learning Representations, 2019

Understanding Posterior Collapse in Generative Latent Variable Models.

James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Revisiting Auxiliary Latent Variables in Generative Models.

Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2018

Soft Actor-Critic Algorithms and Applications.

Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine

CoRR, 2018

Guided evolutionary strategies: escaping the curse of dimensionality in random search.

Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein

CoRR, 2018

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion.

Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Smoothed Action Value Functions for Learning Gaussian Policies.

Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans

Proceedings of the 35th International Conference on Machine Learning, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.

George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling.

Carlos Riquelme, George Tucker, Jasper Snoek

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Hard Alignments with Variational Inference.

Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

An online sequence-to-sequence model for noisy speech recognition.

Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly

CoRR, 2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.

George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Filtering Variational Objectives.

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.

George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

Proceedings of the 5th International Conference on Learning Representations, 2017

Regularizing Neural Networks by Penalizing Confident Output Distributions.

Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton

Proceedings of the 5th International Conference on Learning Representations, 2017

Particle Value Functions.

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Compacting Neural Network Classifiers via Dropout Training.

Yotaro Kubo, George Tucker, Simon Wiesler

CoRR, 2016

Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting.

Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni

Proceedings of the 2016 IEEE Spoken Language Technology Workshop, 2016

Model Compression Applied to Small-Footprint Keyword Spotting.

George Tucker, Minhua Wu, Ming Sun, Sankaran Panchapagesan, Gengshen Fu, Shiv Vitaladevuni

Proceedings of the Interspeech 2016, 2016

2014

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach.

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

BMC Syst. Biol., 2014

2013

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

George Tucker, Po-Ru Loh, Bonnie Berger

BMC Bioinform., 2013