2 papers
stat.ML2025
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim +1
Variational families with full-rank covariance approximations are known not to work well in black-box variational inference (BBVI), both empirically and theoretically. In fact, rec…
cs.LG2024
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Taeyoung Yun +6
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce…