William T. Stephenson — accepted AI-conference papers

2022

Measuring the robustness of Gaussian processes to kernel choice

William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer Deshpande, Tamara Broderick

AISTATS 2022poster

Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of carbon dioxide emissions. Notably, the choice of GP kernel is often somewhat arbitrary. In particular, uncountably many kernels typically align with qualitative prior knowledge (e.…

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2021

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression

William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

NeurIPS 2021poster

Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees. Cross-validation (CV) is widely used for hyperparameter tuning in these models, but do practical methods minimize the true out-of-sample loss? A re…

Cited by 16 Source ↗PDF ↗Scholar ↗

2015

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models

Michael C Hughes, William T Stephenson, Erik Sudderth

NeurIPS 2015poster

Bayesian nonparametric hidden Markov models are typically learned via fixed truncations of the infinite state space or local Monte Carlo proposals that make small changes to the state space. We develop an inference algorithm for the sticky hierarchical Dirichlet process hidden Markov model that scal…

Cited by 25 Source ↗PDF ↗code ↗Scholar ↗