262 citations · 929 across the 33 of their papers we have counts for
39 papers
Data Reuse in Non-Stationary Learning
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider online learning in non-stationary environments, where the goal is to track an unknown parameter that switches abruptly between a finite set of recurring values. Recurre…
Oblivious Learning and Collusive Pricing
Yuhang Wu, Assaf Zeevi
On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical arguments suggest that ignoring competitors induces…
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…
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…
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…