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stat.ML2026
Human-AI Teaming Through the Lens of Calibration
Eric Nalisnick, Chi Zhang, Sophia Qian +1
We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect…
stat.ML2026
Joint Model and Data Sparsification via the Marginal Likelihood
Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2
Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic rel…
stat.ML2025
On Continuous Monitoring of Risk Violations under Unknown Shift
Alexander Timans, Rajeev Verma, Eric Nalisnick +1
Machine learning systems deployed in the real world must operate under dynamic and often unpredictable distribution shifts. This challenges the validity of statistical safety assur…