160 citations · 1.5k across the 122 of their papers we have counts for
13 papers · 1 filter
Randomized Exploration for Non-Stationary Stochastic Linear Bandits
Baekjin Kim, Ambuj Tewari
We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice. The first approach replaces optimism with a…
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari +1
Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without…
Thompson Sampling in Non-Episodic Restless Bandits
Young Hun Jung, Marc Abeille, Ambuj Tewari
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algori…
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
Jack Goetz, Ambuj Tewari
Active learning seeks to build the best possible model with a budget of labelled data by sequentially selecting the next point to label. However the training set is no longer \text…
Online Boosting for Multilabel Ranking with Top-k Feedback
Vinod Raman, Daniel T. Zhang, Young Hun Jung +1
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We…
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
Laura Niss, Ambuj Tewari
Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with -contaminated rewards. We allow an adversary to g…