1 citations · 1 across the 6 of their papers we have counts for
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Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization
Kukyoung Jang, Taehyun Cho, Junrui Zhang +2
Probabilistic smoothing is a standard tool for global optimization, but existing methods rely on Gaussian kernels and specific transforms, often resulting in strong hyperparameter…
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
Taehyun Cho, Seokhun Ju, Seungyub Han +3
To design rewards that align with human goals, Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent technique for learning reward functions from human prefe…
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
Taehyun Cho, Seungyub Han, Seokhun Ju +3
Distributional reinforcement learning improves performance by capturing environmental stochasticity, but a comprehensive theoretical understanding of its effectiveness remains elus…
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
Dohyeong Kim, Taehyun Cho, Seungyub Han +3
The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-base…
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Taehyun Cho, Seungyub Han, Heesoo Lee +2
Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty. However, using the es…