2.6k citations · 8.6k across the 77 of their papers we have counts for
80 papers
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy +2
Active learning is perhaps most naturally posed as an online learning problem. However, prior active learning approaches with deep neural networks assume offline access to the enti…
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra +4
Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks. Yet, it is still un…
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Riashat Islam, Manan Tomar, Alex Lamb +8
Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major…
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan J. Foster, John Langford +1
A central problem in sequential decision making is to develop algorithms that are practical and computationally efficient, yet support the use of flexible, general-purpose models.…
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni +7
In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information. For…
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan J. Foster, Dipendra Misra +2
In real-world reinforcement learning applications the learner's observation space is ubiquitously high-dimensional with both relevant and irrelevant information about the task at h…