1 citations · 3 across the 9 of their papers we have counts for
10 papers
SoftServe: A Scalable Quasi-Newton Method for Deep Learning
Joohwan Ko, Tetiana Parshakova, Diana Cai +1
Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning:…
Amortized Factor Inference Networks for Posterior Inference
Joohwan Ko, Justin Domke
Amortized inference promises fast test-time Bayesian inference, but existing methods typically target a fixed model or a restricted model family. Extending amortization to new mode…
Latent Target Score Matching, with an application to Simulation-Based Inference
Joohwan Ko, Tomas Geffner
Denoising score matching (DSM) for training diffusion models may suffer from high variance at low noise levels. Target Score Matching (TSM) mitigates this when clean data scores ar…
Relaxed Sequence Sampling for Diverse Protein Design
Joohwan Ko, Aristofanis Rontogiannis, Yih-En Andrew Ban +2
Protein design using structure prediction models such as AlphaFold2 has shown remarkable success, but existing approaches like relaxed sequence optimization (RSO) rely on single-pa…
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
Variational inference often struggles with the posterior geometry exhibited by complex hierarchical Bayesian models. Recent advances in flow-based variational families and Variatio…
Layer-Adaptive State Pruning for Deep State Space Models
Minseon Gwak, Seongrok Moon, Joohwan Ko +1
Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computation…
Demystifying SGD with Doubly Stochastic Gradients
Kyurae Kim, Joohwan Ko, Yi-An Ma +1
Optimization objectives in the form of a sum of intractable expectations are rising in importance (e.g., diffusion models, variational autoencoders, and many more), a setting also…
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…
Modeling Choice via Self-Attention
Joohwan Ko, Andrew A. Li
Models of choice are a fundamental input to many now-canonical optimization problems in the field of Operations Management, including assortment, inventory, and price optimization.…
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…