7 papers · 1 filter
Toward Simultaneously Optimal Regret in U-Calibration
Rafael Frongillo, Haipeng Luo, Nishant A. Mehta +1
U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper l…
Generalized Distributional Alignment Games for Unbiased Answer-Level Fine-Tuning
Mehryar Mohri, Jon Schneider, Yutao Zhong
The Distributional Alignment Game framework provides a powerful variational perspective on Answer-Level Fine-Tuning (ALFT). However, standard algorithms for these games rely on est…
Distributional Alignment Games for Answer-Level Fine-Tuning
Mehryar Mohri, Jon Schneider, Yifan Wu
We focus on the problem of \emph{Answer-Level Fine-Tuning} (ALFT), where the goal is to optimize a language model based on the correctness or properties of its final answers, rathe…
Next-Token Prediction and Regret Minimization
Mehryar Mohri, Clayton Sanford, Jon Schneider +2
We consider the question of how to employ next-token prediction algorithms in adversarial online decision-making environments. Specifically, if we train a next-token prediction mod…
Efficient Opportunistic Approachability
Teodor Vanislavov Marinov, Mehryar Mohri, Princewill Okoroafor +2
We study the problem of opportunistic approachability: a generalization of Blackwell approachability where the learner would like to obtain stronger guarantees (i.e., approach a sm…
Near-optimal Swap Regret Minimization for Convex Losses
Lunjia Hu, Jon Schneider, Yifan Wu
We give a randomized online algorithm that guarantees near-optimal expected swap regret against any sequence of adaptively chosen Lipschitz convex losse…
Swap Regret Minimization Through Response-Based Approachability
Ioannis Anagnostides, Gabriele Farina, Maxwell Fishelson +2
We consider the problem of minimizing different notions of swap regret in online optimization. These forms of regret are tightly connected to correlated equilibrium concepts in gam…