119 citations · 172 across the 3 of their papers we have counts for
3 papers · 1 filter
Efficient Optimal Learning for Contextual Bandits
Miroslav Dudik, Daniel Hsu, Satyen Kale +4
We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We…
Parallel Online Learning
Daniel Hsu, Nikos Karampatziakis, John Langford +1
In this work we study parallelization of online learning, a core primitive in machine learning. In a parallel environment all known approaches for parallel online learning lead to…
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…