2 citations · 2 across the 2 of their papers we have counts for
5 papers
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…
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…
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos +2
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we dem…
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…
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy +2
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitou…