228 citations · 798 across the 24 of their papers we have counts for
27 papers
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
Yuwei Luo, Mohsen Bayati
This paper is motivated by recent research in the -dimensional stochastic linear bandit literature, which has revealed an unsettling discrepancy: algorithms like Thompson sampli…
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Mohsen Bayati, Junyu Cao, Wanning Chen
Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from period…
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes
Mohamad Kazem Shirani Faradonbeh, Sadegh Shirani, Mohsen Bayati
Linear diffusion processes serve as canonical continuous-time models for dynamic decision-making under uncertainty. These systems evolve according to drift matrices that specify th…
Learning to Recommend Using Non-Uniform Data
Wanning Chen, Mohsen Bayati
Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines. One complication in this learning task is th…
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling
Nima Hamidi, Mohsen Bayati
In this note, we introduce a general version of the well-known elliptical potential lemma that is a widely used technique in the analysis of algorithms in sequential learning and d…
On Frequentist Regret of Linear Thompson Sampling
Nima Hamidi, Mohsen Bayati
This paper studies the stochastic linear bandit problem, where a decision-maker chooses actions from possibly time-dependent sets of vectors in and receives noisy re…