Assistant Professor, Pohang University of Science and Technology
2 papers at NeurIPS 2025
Label noise in CBMs cripples prediction performance, interpretability, and interventions via a few susceptible concepts. We combat this with sharpness-aware training and entropy-based concept correction, restoring the robustness of CBMs.
We propose a benchmark and method for continual multimodal knowledge editing with reliable compositional reasoning.