7 citations · 29 across the 42 of their papers we have counts for
8 papers · 1 filter
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
Rohan Deb, Aadirupa Saha
We consider the problem of reward maximization in the dueling bandit setup along with constraints on resource consumption. As in the classic dueling bandits, at each round the lear…
Faster Convergence with Multiway Preferences
Aadirupa Saha, Vitaly Feldman, Tomer Koren +1
We address the problem of convex optimization with preference feedback, where the goal is to minimize a convex function given a weaker form of comparison queries. Each query consis…
Federated Online and Bandit Convex Optimization
Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha +1
We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on machines working in parallel…
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1
We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the…
Dueling Optimization with a Monotone Adversary
Avrim Blum, Meghal Gupta, Gene Li +3
We introduce and study the problem of dueling optimization with a monotone adversary, which is a generalization of (noiseless) dueling convex optimization. The goal is to design an…
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
Avrim Blum, Princewill Okoroafor, Aadirupa Saha +1
We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this…
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling
Aadirupa Saha, Branislav Kveton
Most bandit algorithms assume that the reward variances or their upper bounds are known, and that they are the same for all arms. This naturally leads to suboptimal performance and…
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao, Lee Cohen, Avrim Blum +3
In classic reinforcement learning (RL) and decision making problems, policies are evaluated with respect to a scalar reward function, and all optimal policies are the same with reg…