154 citations · 1k across the 49 of their papers we have counts for
8 papers · 1 filter
Non-Stationary Latent Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +4
Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fas…
CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow +3
We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given on…
Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow +2
While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in ma…
Control-Aware Representations for Model-based Reinforcement Learning
Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh
A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE)…
Variational Model-based Policy Optimization
Yinlam Chow, Brandon Cui, MoonKyung Ryu +1
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the dat…
Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer +3
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to i…
Non-Stationary Off-Policy Optimization
Joey Hong, Branislav Kveton, Manzil Zaheer +2
Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-s…
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow +5
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding…