We consider contextual bandits with linear constraints (CBwLC), a variant of contextual
bandits in which the algorithm consumes multiple resources subject to linear constraints on …

This paper studies the problem of stochastic continuum-armed bandit with constraints
(SCBwC), where we optimize a black-box reward function $ f (x) $ subject to a black-box …

We consider a non-stationary Bandits with Knapsack problem. The outcome distribution at
each time is scaled by a non-stationary quantity that signifies changing demand volumes …

Abstract Bandits with Knapsacks (BwK), the generalization of the Multi-Armed Bandits
problem under global budget constraints, has received a lot of attention in recent years. It …

Abstract Best Arm Identification (BAI) is a general online pure exploration framework to
identify optimal decisions among candidates via sequential interactions. We pioneer the …

We consider dynamic pricing with covariates under a generalized linear demand model: a
seller can dynamically adjust the price of a product over a horizon of $ T $ time periods, and …

We consider an online two-stage stochastic optimization with long-term constraints over a
finite horizon of $ T $ periods. At each period, we take the first-stage action, observe a model …

This paper studies safe Reinforcement Learning (safe RL) with linear function approximation
and under hard instantaneous constraints where unsafe actions must be avoided at each …

We study the contextual bandits with knapsack (CBwK) problem under the high-dimensional
setting where the dimension of the feature is large. The reward of pulling each arm equals …

In this paper, we study the problem of watermarking large language models (LLMs). We
consider the trade-off between model distortion and detection ability and formulate it as a …