A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis. - George Tucker; Po-Ru Loh; Bonnie Berger (BMC Bioinform., 2013)

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. - Pablo Meyer 001; Thomas Cokelaer; Deepak Chandran; Kyung Hyuk Kim; Po-Ru Loh; George Tucker; Mark Lipson; Bonnie Berger; Clemens Kreutz; Andreas Raue; Bernhard Steiert; Jens Timmer; et al. (BMC Syst. Biol., 2014)

Model Compression Applied to Small-Footprint Keyword Spotting. - George Tucker; Minhua Wu; Ming Sun 007; Sankaran Panchapagesan; Gengshen Fu; Shiv Vitaladevuni (INTERSPEECH, 2016)

Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting. - Ming Sun 007; Anirudh Raju; George Tucker; Sankaran Panchapagesan; Gengshen Fu; Arindam Mandal; Spyros Matsoukas; Nikko Strom; Shiv Vitaladevuni (SLT, 2016)

Compacting Neural Network Classifiers via Dropout Training. - Yotaro Kubo; George Tucker; Simon Wiesler (CoRR, 2016)

Particle Value Functions. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Arnaud Doucet; Andriy Mnih; Yee Whye Teh (ICLR (Workshop), 2017)

Regularizing Neural Networks by Penalizing Confident Output Distributions. - Gabriel Pereyra; George Tucker; Jan Chorowski; Lukasz Kaiser; Geoffrey E. Hinton (ICLR (Workshop), 2017)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. - George Tucker; Andriy Mnih; Chris J. Maddison; Jascha Sohl-Dickstein (ICLR (Workshop), 2017)

Filtering Variational Objectives. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Mohammad Norouzi 002; Andriy Mnih; Arnaud Doucet; Yee Whye Teh (NIPS, 2017)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. - George Tucker; Andriy Mnih; Chris J. Maddison; Dieterich Lawson; Jascha Sohl-Dickstein (NIPS, 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)

Learning Hard Alignments with Variational Inference. - Dieterich Lawson; Chung-Cheng Chiu; George Tucker; Colin Raffel; Kevin Swersky; Navdeep Jaitly (ICASSP, 2018)

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. - Carlos Riquelme; George Tucker; Jasper Snoek (ICLR (Poster), 2018)

Smoothed Action Value Functions for Learning Gaussian Policies. - Ofir Nachum; Mohammad Norouzi 002; George Tucker; Dale Schuurmans (ICML, 2018)

The Mirage of Action-Dependent Baselines in Reinforcement Learning. - George Tucker; Surya Bhupatiraju; Shixiang Gu; Richard E. Turner; Zoubin Ghahramani; Sergey Levine (ICML, 2018)

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. - Jacob Buckman; Danijar Hafner; George Tucker; Eugene Brevdo; Honglak Lee (NeurIPS, 2018)

Guided evolutionary strategies: escaping the curse of dimensionality in random search. - Niru Maheswaranathan; Luke Metz; George Tucker; Jascha Sohl-Dickstein (CoRR, 2018)

Soft Actor-Critic Algorithms and Applications. - Tuomas Haarnoja; Aurick Zhou; Kristian Hartikainen; George Tucker; Sehoon Ha; Jie Tan 001; Vikash Kumar; Henry Zhu; Abhishek Gupta 004; Pieter Abbeel; Sergey Levine (CoRR, 2018)

Revisiting Auxiliary Latent Variables in Generative Models. - Dieterich Lawson; George Tucker; Bo Dai 001; Rajesh Ranganath (DGS@ICLR, 2019)

Understanding Posterior Collapse in Generative Latent Variable Models. - James Lucas; George Tucker; Roger B. Grosse; Mohammad Norouzi 002 (DGS@ICLR, 2019)

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. - George Tucker; Dieterich Lawson; Shixiang Gu; Chris J. Maddison (ICLR (Poster), 2019)

The Laplacian in RL: Learning Representations with Efficient Approximations. - Yifan Wu; George Tucker; Ofir Nachum (ICLR (Poster), 2019)

Guided evolutionary strategies: augmenting random search with surrogate gradients. - Niru Maheswaranathan; Luke Metz; George Tucker; Dami Choi; Jascha Sohl-Dickstein (ICML, 2019)

On Variational Bounds of Mutual Information. - Ben Poole; Sherjil Ozair; Aäron van den Oord; Alexander A. Alemi; George Tucker (ICML, 2019)

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. - Aviral Kumar; Justin Fu; Matthew Soh; George Tucker; Sergey Levine (NeurIPS, 2019)

Energy-Inspired Models: Learning with Sampler-Induced Distributions. - Dieterich Lawson; George Tucker; Bo Dai 001; Rajesh Ranganath (NeurIPS, 2019)

Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. - James Lucas; George Tucker; Roger B. Grosse; Mohammad Norouzi 002 (NeurIPS, 2019)

Learning to Walk Via Deep Reinforcement Learning. - Tuomas Haarnoja; Sehoon Ha; Aurick Zhou; Jie Tan 001; George Tucker; Sergey Levine (Robotics: Science and Systems, 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; et al. (CoRR, 2019)

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. - Aviral Kumar; Justin Fu; George Tucker; Sergey Levine (CoRR, 2019)

Reinforcement Learning Driven Heuristic Optimization. - Qingpeng Cai 001; Will Hang; Azalia Mirhoseini; George Tucker; Jingtao Wang; Wei Wei (CoRR, 2019)

Behavior Regularized Offline Reinforcement Learning. - Yifan Wu; George Tucker; Ofir Nachum (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; Afroz Mohiuddin; Ryan Sepassi; et al. (ICLR, 2020)

Meta-Learning without Memorization. - Mingzhang Yin; George Tucker; Mingyuan Zhou; Sergey Levine; Chelsea Finn (ICLR, 2020)

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. - Zhe Dong; Andriy Mnih; George Tucker (NeurIPS, 2020)

Conservative Q-Learning for Offline Reinforcement Learning. - Aviral Kumar; Aurick Zhou; George Tucker; Sergey Levine (NeurIPS, 2020)

D4RL: Datasets for Deep Data-Driven Reinforcement Learning. - Justin Fu; Aviral Kumar; Ofir Nachum; George Tucker; Sergey Levine (CoRR, 2020)

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. - Sergey Levine; Aviral Kumar; George Tucker; Justin Fu (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Benchmarks for Deep Off-Policy Evaluation. - Justin Fu; Mohammad Norouzi 002; Ofir Nachum; George Tucker; Ziyu Wang 001; Alexander Novikov 001; Mengjiao Yang 001; Michael R. Zhang; Yutian Chen 001; Aviral Kumar; Cosmin Paduraru; Sergey Levine; et al. (ICLR, 2021)

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. - Michael R. Zhang; Thomas Paine; Ofir Nachum; Cosmin Paduraru; George Tucker; Ziyu Wang 001; Mohammad Norouzi 002 (ICLR, 2021)

Coupled Gradient Estimators for Discrete Latent Variables. - Zhe Dong; Andriy Mnih; George Tucker (NeurIPS, 2021)

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. - Michael R. Zhang; Tom Le Paine; Ofir Nachum; Cosmin Paduraru; George Tucker; Ziyu Wang 001; Mohammad Norouzi 002 (CoRR, 2021)

Offline Policy Selection under Uncertainty. - Mengjiao Yang 001; Bo Dai 001; Ofir Nachum; George Tucker; Dale Schuurmans (AISTATS, 2022)

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. - Aviral Kumar; Rishabh Agarwal; Tengyu Ma 001; Aaron C. Courville; George Tucker; Sergey Levine (ICLR, 2022)

Model Selection in Batch Policy Optimization. - Jonathan Lee 002; George Tucker; Ofir Nachum; Bo Dai 001 (ICML, 2022)

Oracle Inequalities for Model Selection in Offline Reinforcement Learning. - Jonathan N. Lee; George Tucker; Ofir Nachum; Bo Dai 001; Emma Brunskill (NeurIPS, 2022)

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. - Aviral Kumar; Rishabh Agarwal; Xinyang Geng; George Tucker; Sergey Levine (ICLR, 2023)

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. - Yiren Lu 001; Justin Fu; George Tucker; Xinlei Pan; Eli Bronstein; Rebecca Roelofs; Benjamin Sapp; Brandyn White; Aleksandra Faust; Shimon Whiteson; Dragomir Anguelov; Sergey Levine (IROS, 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 001; Jean Harb; Xinlei Pan; Yan Wang 051; Xiangyu Chen 007; John D. Co-Reyes; Rishabh Agarwal; et al. (NeurIPS, 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; et al. (CoRR, 2023)

Guided Evolution with Binary Discriminators for ML Program Search. - John D. Co-Reyes; Yingjie Miao; George Tucker; Aleksandra Faust; Esteban Real (CoRR, 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; et al. (CoRR, 2024)

Training Language Models to Self-Correct via Reinforcement Learning. - 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; et al. (ICLR, 2025)