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cs.LG2025
Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
Jane H. Lee, Baturay Saglam, Spyridon Pougkakiotis +2
Constrained optimization provides a common framework for dealing with conflicting objectives in reinforcement learning (RL). In most of these settings, the objectives (and constrai…
cs.LG2024
TSDS: Data Selection for Task-Specific Model Finetuning
Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
Finetuning foundation models for specific tasks is an emerging paradigm in modern machine learning. The efficacy of task-specific finetuning largely depends on the selection of app…