937 citations · 4k across the 76 of their papers we have counts for
8 papers · 1 filter
Corralling a Band of Bandit Algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur +1
We study the problem of combining multiple bandit algorithms (that is, online learning algorithms with partial feedback) with the goal of creating a master algorithm that performs…
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik
We study the off-policy evaluation problem---estimating the value of a target policy using data collected by another policy---under the contextual bandit model. We consider the gen…
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal +2
This paper studies systematic exploration for reinforcement learning with rich observations and function approximation. We introduce a new model called contextual decision processe…
Making Contextual Decisions with Low Technical Debt
Alekh Agarwal, Sarah Bird, Markus Cozowicz +9
Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms suc…
Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal +4
This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendat…
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
David Abel, Alekh Agarwal, Fernando Diaz +2
High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of…