5 papers · 1 filter
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…
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar +2
We study the linear bandit problem that accounts for partially observable features. Without proper handling, unobserved features can lead to linear regret in the decision horizon $…
Learning the Pareto Front Using Bootstrapped Observation Samples
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vec…