2.6k citations · 8.6k across the 77 of their papers we have counts for
7 papers · 1 filter
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…
Logarithmic Time One-Against-Some
Hal Daume, Nikos Karampatziakis, John Langford +1
We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. Comp…
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…
Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford +1
We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not). The Search oracle models the situation where a human search…
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
We propose and study a new model for reinforcement learning with rich observations, generalizing contextual bandits to sequential decision making. These models require an agent to…