Haksoo Lim, Myeongjin Lee, Wonjoon Chang, and Jaesik Choi Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis European Conference on Computer Vision (ECCV) 2026.
Soyeon Kim, Kyowoon Lee, and Jaesik Choi Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution European Conference on Computer Vision (ECCV) 2026.
Sehyun Lee, Dahee Kwon, Damin Lee, and Jaesik Choi SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanations European Conference on Computer Vision (ECCV) 2026.
Spectral Integrated Gradients for Coarse-to-Fine Feature Attribution, led by Soyeon Kim, Seongwoo Lim, Kyowoon Lee, and Jaesik Choi is accepted at KDD 2026.
A paper, Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution by Soyeon Kim, Seongwoo Lim, Kyowoon Lee, and Jaesik Choi is accepted at International Conference on Machine Learning (ICML), 2026.
A paper, Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation by Dahee Kwon, Haeun Lee, and Jaesik Choi is accepted at International Conference on Machine Learning (ICML), 2026.
A paper, DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction by Enver Menadjiev, Jihyeon Seong, Jisu Yeo, and Jaesik Choi is accepted at International Conference on Machine Learning (ICML), 2026.
A paper, Incomplete Prompt Jailbreaks in Large Language Models, written by Yeonjea Kim, Bumjin Park, and Jaesik Choi is accepted at Findings of ACL 2026.
A paper, K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology, written by Soyeon, Cheongwoong, Myeongjin Lee, and Jaesik Choi is accepted at Findings of ACL 2026.
A paper, Towards Transparent Time Series Analysis: Exploring Methods and Enhancing Interpretability, written by Youngjin Park, Anh Tong, Sehyun Lee, Jihyeon Seong, Qin Xie, and Jaesik Choi, is accepted at ACM Computing Surveys, 2026.
