34 citations · 169 across the 29 of their papers we have counts for
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stat.ML2026
Reliable AI-Assisted Decisions via Selective Auditing
Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani +1
We consider online monitoring of an AI system that produces a sequence of decisions. At each round, the system outputs a proposed decision together with a confidence score estimati…
stat.ML2026
High-Value Training Data from Noisy, Diverse Sources
Hedi Driss, Evan Frick, Anastasios N. Angelopoulos +4
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Data…