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cs.LG2025
SAVA: Scalable Learning-Agnostic Data Valuation
Samuel Kessler, Tam Le, Vu Nguyen
Selecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual d…
cs.LG2025
On Sequential Bayesian Inference for Continual Learning
Samuel Kessler, Adam Cobb, Tim G. J. Rudner +2
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revis…
cs.LG2024
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured mo…