44 citations · 128 across the 20 of their papers we have counts for
14 papers · 1 filter
Contextual Dynamic Pricing with Heterogeneous Buyers
Thodoris Lykouris, Sloan Nietert, Princewill Okoroafor +2
We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over rounds) that depend on the observabl…
Learning to Defer in Congested Systems: The AI-Human Interplay
Thodoris Lykouris, Wentao Weng
High-stakes applications rely on combining Artificial Intelligence (AI) and humans for responsive and reliable decision making. For example, content moderation in social media plat…
The Transient Cost of Learning in Queueing Systems
Daniel Freund, Thodoris Lykouris, Wentao Weng
Queueing systems are widely applicable stochastic models with use cases in communication networks, healthcare, service systems, etc. Although their optimal control has been extensi…
Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
Daniel Freund, Thodoris Lykouris, Wentao Weng
We study decentralized multi-agent learning in bipartite queueing systems, a standard model for service systems. In particular, N agents request service from K servers in a fully d…
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…
Constrained episodic reinforcement learning in concave-convex and knapsack settings
Kianté Brantley, Miroslav Dudik, Thodoris Lykouris +4
We propose an algorithm for tabular episodic reinforcement learning with constraints. We provide a modular analysis with strong theoretical guarantees for settings with concave rew…
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…
Contextual Search in the Presence of Adversarial Corruptions
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata +1
We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard formulations of this pro…
Corruption-robust exploration in episodic reinforcement learning
Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins +1
We initiate the study of multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system ext…
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…
Small-loss bounds for online learning with partial information
Thodoris Lykouris, Karthik Sridharan, Eva Tardos
We consider the problem of adversarial (non-stochastic) online learning with partial information feedback, where at each round, a decision maker selects an action from a finite set…