83 citations · 126 across the 30 of their papers we have counts for
4 papers · 1 filter
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Neil K. Chada, Simon Cotter, Kody J. H. Law +1
Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown quantities t…
Information-Theoretic Foundations of Online Temperature Sampling
Jin Sima, Nikolaos Papagiannis, Ananth Grama +1
Repetitive, degenerate generation is a persistent failure of LLM decoding, and the standard remedies (temperature, nucleus, and top- sampling) are static heuristics. By Kac's le…
Robustness of Diffusion Models under Distribution Shift
Wei Luo, Neil K. Chada, Shijie Zhang +1
Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guar…
Kinetic Langevin Splitting Schemes for Constrained Sampling
Neil K. Chada, Lu Yu
Constrained sampling is an important and challenging task in computational statistics, concerned with generating samples from a distribution under certain constraints. There are nu…