1 citations · 2 across the 11 of their papers we have counts for
12 papers
From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models
Hyeonggeun Han, Taehyun Cho, Sangwoo Hong
Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-Guided Vis…
A Regret Minimization Framework on Preference Learning in Large Language Models
Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim +4
Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness sig…
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…
Why Latent Actions Fail, and How to Prevent It
Jung Min Lee, Taehyun Cho, Li Zhao +1
Latent action models (LAMs) aim to learn action-like representations from unlabeled videos by compressing frame-to-frame changes. The frames of in-the-wild videos, however, contain…
MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
Jung Min Lee, Dohyeok Lee, Seokhun Ju +5
Latent actions learned from diverse human videos serve as pseudo-labels for vision-language-action (VLA) pretraining, but provide effective supervision only if they remain informat…
Learning Robust Visuomotor Policies via Dynamics Alignment
Dohyeok Lee, Jung Min Lee, Munkyung Kim +8
Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would produce, so they cannot tell whether it r…
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…
On the Convergence of Continual Learning with Adaptive Methods
Seungyub Han, Yeongmo Kim, Taehyun Cho +1
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the concep…
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
Dohyeok Lee, Seungyub Han, Taehyun Cho +1
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-o…
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…