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cs.LG2026
Adaptive Calibration in Non-Stationary Environments
Junyan Liu, Haipeng Luo, Lillian J. Ratliff
Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environments where outcomes may be ar…
cs.LG2026
Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation
Junyan Liu, Haipeng Luo, Zihan Zhang +1
We study online learning in two-player uninformed Markov games, where the opponent's actions and policies are unobserved. In this setting, Tian et al. (2021) show that achieving no…
cs.GT2026
Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
Arnab Maiti, Junyan Liu, Kevin Jamieson +1
We study the discrete Bertrand pricing game with a non-increasing demand function. The game has players who simultaneously choose prices from the set $\{1/k, 2/k, \ldots,…