78 citations · 333 across the 57 of their papers we have counts for
7 papers · 1 filter
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games
Georgios Piliouras, Lillian Ratliff, Ryann Sim +1
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EF…
Instance-optimal PAC Algorithms for Contextual Bandits
Zhaoqi Li, Lillian Ratliff, Houssam Nassif +2
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remai…
Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback
Adhyyan Narang, Omid Sadeghi, Lillian J Ratliff +2
In the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives. This includes…
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff +2
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in t…
General sum stochastic games with networked information flows
Sarah H. Q. Li, Lillian J. Ratliff, Peeyush Kumar
Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these…
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments
Mitas Ray, Dmitriy Drusvyatskiy, Maryam Fazel +1
This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to…