arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round...
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv:2602. 09456v2 Announce Type: replace Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression.
arXiv:2402. 05002v3 Announce Type: replace Abstract: The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback.
arXiv:2602. 02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms.
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
The paper introduces EXPO, an algorithm that automatically optimizes the meta-prompt—specifically the task description and meta-instruction—for large language model agents in sequential decision-making tasks such as Bayesian optimization and multi-armed bandits. Building on adversarial bandit techniques to handle non-stationary rewards, the authors extend EXPO to EXPO-ES, which also optimizes exemplars (historical interactions) within the meta-prompt. Experiments demonstrate that these methods significantly improve the performance of LLM-based agents in sequential decision-making scenarios.
arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.
The article outlines a Ph.D. research agenda aimed at creating provably efficient and practical algorithms for data‑driven sequential decision‑making under uncertainty. It focuses on reinforcement learning and multi‑armed bandits, targeting applications such as recommendation systems, computer networks, video analytics, and large language models. The work seeks to overcome limitations of existing methods—such as reliance on idealized models, lack of robustness to adversarial perturbations, and poor instance‑dependent performance—by developing algorithms that are more efficient, robust, instance‑adaptive, and generalizable to new environments.
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.
arXiv:2606. 01081v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy.
The paper introduces online contextual matrix games, a framework that merges contextual bandits with multi‑player online games to handle dynamic contexts and strategic interactions. It presents OnGameLearn, an algorithm that balances exploration and exploitation across actions and contexts, providing statistical guarantees such as tail bounds, Nash equilibrium convergence, asymptotic normality, and sublinear regret. The authors also define a policy value for matrix games and propose a doubly robust, √T‑consistent estimator, demonstrating effectiveness through simulations and a hotel pricing case study.