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cs.LG2025
Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning
Zhizhong Zhao, Ke Chen
Uncertainty quantification (UQ) is vital for trustworthy deep learning, yet existing methods are either computationally intensive, such as Bayesian or ensemble methods, or provide…
cs.LG2024
Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning
Lisheng Wu, Ke Chen
Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards. A primary li…