8 citations · 9 across the 3 of their papers we have counts for
7 papers · 1 filter
AutoEval Done Right: Using Synthetic Data for Model Evaluation
Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef +2
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of h…
Cost-Optimal Active AI Model Evaluation
Anastasios N. Angelopoulos, Jacob Eisenstein, Jonathan Berant +2
The development lifecycle of generative AI systems requires continual evaluation, data acquisition, and annotation, which is costly in both resources and time. In practice, rapid i…
Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos +2
Many applications of machine learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what…
Automatically Adaptive Conformal Risk Control
Vincent Blot, Anastasios N Angelopoulos, Michael I Jordan +1
Science and technology have a growing need for effective mechanisms that ensure reliable, controlled performance from black-box machine learning algorithms. These performance guara…
Gradient Equilibrium in Online Learning: Theory and Applications
Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses…
Label Noise Robustness of Conformal Prediction
Bat-Sheva Einbinder, Shai Feldman, Stephen Bates +3
We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, c…