activity
20242026
most citedAligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.NC2026

Decoding Error-Related Potentials under Multisensory Feedback with Varying Congruency

Yixin Liu, Kang Yin, Hye-Bin Shin +1

Error-related potentials (ErrPs) are widely studied neural signatures associated with error processing in human-machine interaction. In realistic settings, error perception often o…

cs.LG2025

Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces

Hyo-Jeong Jang, Hye-Bin Shin, Seong-Whan Lee

Electroencephalography (EEG) is a fundamental modality for cognitive state monitoring in brain-computer interfaces (BCIs). However, it is highly susceptible to intrinsic signal err…

cs.RO20252 cited

Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback

Suzie Kim, Hye-Bin Shin, Seong-Whan Lee

Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-spe…

cs.AI2025

Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition

Lintong Zhang, Kang Yin, Seong-Whan Lee

Attribution-based explanation techniques capture key patterns to enhance visual interpretability; however, these patterns often lack the granularity needed for insight in fine-grai…

cs.CV2024

EEG-based Multimodal Representation Learning for Emotion Recognition

Kang Yin, Hye-Bin Shin, Dan Li +1

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availab…