218 citations · 909 across the 108 of their papers we have counts for
10 papers · 1 filter
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya K. Singh, DJ Strouse +4
Large language models are typically aligned with human preferences by optimizing (RMs) fitted to human feedback. However, human preferences are multi-facet…
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
Yongyuan Liang, Yanchao Sun, Ruijie Zheng +5
Deploying reinforcement learning (RL) systems requires robustness to uncertainty and model misspecification, yet prior robust RL methods typically only study noise introduced indep…
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the conver…
Improved Sample Complexity Bounds for Branch-and-Cut
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm +1
Branch-and-cut is the most widely used algorithm for solving integer programs, employed by commercial solvers like CPLEX and Gurobi. Branch-and-cut has a wide variety of tunable pa…
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights U…
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
Maria-Florina Balcan, Dan DeBlasio, Travis Dick +3
Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit mea…