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cs.LG2026
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:…
cs.LG2026
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…
cs.LG2026
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…