2k citations · 5.9k across the 38 of their papers we have counts for
9 papers · 1 filter
Meta-Learning without Memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou +2
The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged a…
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, Ofir Nachum
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment. However in many real-world applications, access to the en…
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
James Lucas, George Tucker, Roger Grosse +1
Posterior collapse in Variational Autoencoders (VAEs) arises when the variational posterior distribution closely matches the prior for a subset of latent variables. This paper pres…
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson, George Tucker, Bo Dai +1
Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in E…
Reinforcement Learning Driven Heuristic Optimization
Qingpeng Cai, Will Hang, Azalia Mirhoseini +3
Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, th…
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, George Tucker +1
Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximat…
On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron van den Oord +2
Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and s…
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos +11
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically require…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods…