2.6k citations · 8.6k across the 77 of their papers we have counts for
6 papers · 2 filters
Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
An importance weight quantifies the relative importance of one example over another, coming up in applications of boosting, asymmetric classification costs, reductions, and active…
Agnostic Active Learning Without Constraints
Alina Beygelzimer, Daniel Hsu, John Langford +1
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate…
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford +1
Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the e…
A Contextual-Bandit Approach to Personalized News Article Recommendation
Lihong Li, Wei Chu, John Langford +1
Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few…
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Sham Kakade +1
We provide a sound and consistent foundation for the use of \emph{nonrandom} exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chose…
Contextual Bandit Algorithms with Supervised Learning Guarantees
Alina Beygelzimer, John Langford, Lihong Li +2
We address the problem of learning in an online, bandit setting where the learner must repeatedly select among actions, but only receives partial feedback based on its choices.…