8 papers
Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion
Yuhang Wu, Assaf Zeevi
On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical learning arguments suggest an affirmative answer: i…
The Cost of Learning Under Multiple Change Points
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high conf…
Adaptive Querying with AI Persona Priors
Kaizheng Wang, Yuhang Wu, Assaf Zeevi
We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets. Classica…
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1
Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad applicability. While existing studi…
A Broader View of Thompson Sampling
Yanlin Qu, Hongseok Namkoong, Assaf Zeevi
Thompson Sampling is one of the most widely used and studied bandit algorithms, known for its simple structure, low regret performance, and solid theoretical guarantees. Yet, in st…
Optimal single threshold stopping rules and sharp prophet inequalities
Alexander Goldenshluger, Yaakov Malinovsky, Assaf Zeevi
This paper considers a finite horizon optimal stopping problem for a sequence of independent and identically distributed random variables, where the objective is to design stopping…