Information-Theoretic State Space Model for Multi-View Reinforcement Learning. - HyeongJoo Hwang; Seokin Seo; Youngsoo Jang; Sungyoon Kim; Geon-Hyeong Kim; Seunghoon Hong; Kee-Eung Kim (ICML, 2023)

Isotropic Representation Can Improve Dense Retrieval. - Euna Jung; Jungwon Park; Jaekeol Choi; Sungyoon Kim; Wonjong Rhee (PAKDD (3), 2023)

Stitching Sub-trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL. - Sungyoon Kim; Yunseon Choi; Daiki E. Matsunaga; Kee-Eung Kim (AAAI, 2024)

Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time. - Sungyoon Kim; Mert Pilanci (ICML, 2024)

Text-Driven Prototype Learning for Few-Shot Class-Incremental Learning. - Seongbeom Park; Haeji Jung; Daewon Chae; Hyunju Yun; Sungyoon Kim; Suhong Moon; Jinkyu Kim 001; Seunghyun Park 001 (ICPR (9), 2024)

Randomized Geometric Algebra Methods for Convex Neural Networks. - Yifei Wang 005; Sungyoon Kim; Paul Chu; Indu Subramaniam; Mert Pilanci (CoRR, 2024)

LaDiMo: Layer-wise Distillation Inspired MoEfier. - Sungyoon Kim; Youngjun Kim; Kihyo Moon; Minsung Jang (CoRR, 2024)

Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality. - Sungyoon Kim; Aaron Mishkin; Mert Pilanci (ICLR, 2025)

MatRL: Provably Generalizable Iterative Algorithm Discovery via Monte-Carlo Tree Search. - Sungyoon Kim; Rajat Vadiraj Dwaraknath; Longling Geng; Mert Pilanci (CoRR, 2025)