937 citations · 4k across the 76 of their papers we have counts for
6 papers · 1 filter
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy +2
We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-opti…
Practical Contextual Bandits with Regression Oracles
Dylan J. Foster, Alekh Agarwal, Miroslav Dudík +2
A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded. We present a new technique that has…
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík +2
We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach enco…
On Oracle-Efficient PAC RL with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy +3
We study the computational tractability of PAC reinforcement learning with rich observations. We present new provably sample-efficient algorithms for environments with deterministi…
Hierarchical Imitation and Reinforcement Learning
Hoang M. Le, Nan Jiang, Alekh Agarwal +3
We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically…
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally…