← Authors

George Tucker

29 papers · 2016–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) πŸ—ΊοΈ Taxonomy Completionist (37) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (9) πŸ‘‘ Triple Crown πŸ‘₯ Mega-Team (22) πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (88) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (8) πŸ’Ž Century Club (29)

Conferences

NIPS (11) ICLR (10) ICML (5) AISTATS (1) INTERSPEECH (1) RSS (1)

Top co-authors

Sergey Levine (9) Ofir Nachum (7) Aviral Kumar (6) Mohammad Norouzi (5) Bo Dai (4) Andriy Mnih (4) Rishabh Agarwal (4) Justin Fu (3) John Lawson (3) Cosmin Paduraru (3)

Research topics

Reinforcement Learning (1) Robotics (1) Optimization & Theory (1)

Keywords

variational inference (5) offline reinforcement learning (4) variance reduction (4) continuous control (3) sample efficiency (3) gradient estimator (3) marginal likelihood (2) evidence lower bound (2) generative model (2) reinforcement learning (2) off-policy learning (2) model selection (2) policy gradient (2) deep reinforcement learning (2) importance sampling (2) discrete latent variable (2) uncertainty quantification (1) policy evaluation (1) ensemble learning (1) generative modeling (1)

Papers

Training Language Models to Self-Correct via Reinforcement Learning ICLR 2025
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research NIPS 2023
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes ICLR 2023
Model Selection in Batch Policy Optimization ICML 2022
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization ICLR 2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning NIPS 2022
Offline Policy Selection under Uncertainty AISTATS 2022
Benchmarks for Deep Off-Policy Evaluation ICLR 2021
Coupled Gradient Estimators for Discrete Latent Variables NIPS 2021
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization ICLR 2021
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables NIPS 2020
Conservative Q-Learning for Offline Reinforcement Learning NIPS 2020
Model Based Reinforcement Learning for Atari ICLR 2020
Meta-Learning without Memorization ICLR 2020
Learning to Walk Via Deep Reinforcement Learning RSS 2019
Energy-Inspired Models: Learning with Sampler-Induced Distributions NIPS 2019
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse NIPS 2019
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction NIPS 2019
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives ICLR 2019
The Laplacian in RL: Learning Representations with Efficient Approximations ICLR 2019
Guided evolutionary strategies: augmenting random search with surrogate gradients ICML 2019
On Variational Bounds of Mutual Information ICML 2019
The Mirage of Action-Dependent Baselines in Reinforcement Learning ICML 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling ICLR 2018
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion NIPS 2018
Smoothed Action Value Functions for Learning Gaussian Policies ICML 2018
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NIPS 2017
Filtering Variational Objectives NIPS 2017
Model Compression Applied to Small-Footprint Keyword Spotting INTERSPEECH 2016