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stat.ML2026
Horseshoe Mixtures-of-Experts (HS-MoE)
Nick Polson, Vadim Sokolov
Horseshoe mixtures-of-experts (HS-MoE) models provide a Bayesian framework for sparse expert selection in mixture-of-experts architectures. We combine the horseshoe prior's adaptiv…
stat.ML2025
Generative Bayesian Hyperparameter Tuning
Hedibert Lopes, Nick Polson, Vadim Sokolov
\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-vali…
stat.ML2025
Bayesian Double Descent
Nick Polson, Vadim Sokolov
Double descent is a phenomenon of over-parameterized statistical models such as deep neural networks which have a re-descending property in their risk function. As the complexity o…