160 citations · 1.5k across the 122 of their papers we have counts for
6 papers · 1 filter
Feature Clustering for Accelerating Parallel Coordinate Descent
Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar +1
Large-scale L1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classifi…
The Interplay Between Stability and Regret in Online Learning
Ankan Saha, Prateek Jain, Ambuj Tewari
This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret). We introduce a novel quantity called {\em forward regret} t…
Deterministic MDPs with Adversarial Rewards and Bandit Feedback
Raman Arora, Ofer Dekel, Ambuj Tewari
We consider a Markov decision process with deterministic state transition dynamics, adversarially generated rewards that change arbitrarily from round to round, and a bandit feedba…
Scaling Up Coordinate Descent Algorithms for Large Regularization Problems
Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari +1
We present a generic framework for parallel coordinate descent (CD) algorithms that includes, as special cases, the original sequential algorithms Cyclic CD and Stochastic CD, as w…
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
Raman Arora, Ofer Dekel, Ambuj Tewari
Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn…
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
Peter L. Bartlett, Ambuj Tewari
We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each ep…