10 citations · 10 across the 1 of their papers we have counts for
7 papers
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy +4
Thompson sampling and other Bayesian sequential decision-making algorithms are among the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The c…
Bandits with adversarial scaling
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme
We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component. Our model captures display advertising where the "click-thr…
Advancing subgroup fairness via sleeping experts
Avrim Blum, Thodoris Lykouris
We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some…
Feedback graph regret bounds for Thompson Sampling and UCB
Thodoris Lykouris, Eva Tardos, Drishti Wali
We study the stochastic multi-armed bandit problem with the graph-based feedback structure introduced by Mannor and Shamir. We analyze the performance of the two most prominent sto…
On preserving non-discrimination when combining expert advice
Avrim Blum, Suriya Gunasekar, Thodoris Lykouris +1
We study the interplay between sequential decision making and avoiding discrimination against protected groups, when examples arrive online and do not follow distributional assumpt…
Stochastic bandits robust to adversarial corruptions
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme
We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of…