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cs.LG2026
When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
Shuqi Liu, Yuzhou Cao, Lei Feng +2
Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that…
cs.LG2024
Reinforcement Learning with LTL and -Regular Objectives via Optimality-Preserving Translation to Average Rewards
Xuan-Bach Le, Dominik Wagner, Leon Witzman +2
Linear temporal logic (LTL) and, more generally, -regular objectives are alternatives to the traditional discount sum and average reward objectives in reinforcement learning (R…