Learning to Scale Logits for Temperature-Conditional GFlowNets. - Minsu Kim 004; Joohwan Ko; Dinghuai Zhang; Ling Pan; Taeyoung Yun; Woochang Kim; Jinkyoo Park; Yoshua Bengio (CoRR, 2023)

Modeling Choice via Self-Attention. - Joohwan Ko; Andrew A. Li (CoRR, 2023)

Demystifying SGD with Doubly Stochastic Gradients. - Kyurae Kim; Joohwan Ko; Yian Ma; Jacob R. Gardner (ICML, 2024)

Learning to Scale Logits for Temperature-Conditional GFlowNets. - Minsu Kim 004; Joohwan Ko; Taeyoung Yun; Dinghuai Zhang; Ling Pan; Woochang Kim; Jinkyoo Park; Emmanuel Bengio; Yoshua Bengio (ICML, 2024)

Provably Scalable Black-Box Variational Inference with Structured Variational Families. - Joohwan Ko; Kyurae Kim; Woochang Kim; Jacob R. Gardner (ICML, 2024)

Enhancing Topological Dependencies in Spatio-Temporal Graphs With Cycle Message Passing Blocks. - Minho Lee; Yun Young Choi; Sun Woo Park; Seunghwan Lee; Joohwan Ko; Jaeyoung Hong (LoG, 2024)

Layer-Adaptive State Pruning for Deep State Space Models. - Minseon Gwak; Seongrok Moon; Joohwan Ko; PooGyeon Park (NeurIPS, 2024)

A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs. - Yun Young Choi; Minho Lee; Sun Woo Park; Seunghwan Lee; Joohwan Ko (CoRR, 2024)

Model Informed Flows for Bayesian Inference of Probabilistic Programs. - Joohwan Ko; Justin Domke (CoRR, 2025)