Modern recommender systems lie at the heart of complex ecosystems that couple the
behavior of users, content providers, advertisers, and other actors. Despite this, the focus of …

We present a novel unified bilevel optimization-based framework,\textsf {PARL}, formulated
to address the recently highlighted critical issue of policy alignment in reinforcement …

Large Language Models (LLMs) like GPT-4 have revolutionized natural language
processing, showing remarkable linguistic proficiency and reasoning capabilities. However …

Dynamic mechanism design is a challenging extension to ordinary mechanism design in
which the mechanism designer must make a sequence of decisions over time in the face of …

Dynamic mechanism design has garnered significant attention from both computer scientists
and economists in recent years. By allowing agents to interact with the seller over multiple …

For the dissertation, we propose studying efficiently learning the optimal dynamic
mechanism when the agents' valuations can be characterized by a Markov Decision …

In reinforcement learning (RL), a reward function is often assumed at the outset of a policy
optimization procedure. Learning in such a fixed reward paradigm in RL can neglect …