Anastasios Angelopoulos

Ph.D. student in Electrical Engineering and Computer Science
at the University of California, Berkeley

Student of Michael I. Jordan and of Jitendra Malik

Research interests

Building out Distribution-Free Uncertainty Quantification, motivated by applications to medicine and computational imaging.

Recent News

October, 2022 I was lucky to collaborate with Bat-Sheva Einbinder, who showed that conformal prediction is robust to certain types of label noise in her new paper. Take a look!

September, 2022 We made a major update to the Gentle Intro. Check out the new codebase here!

August, 2022 Check out our paper, Conformal Risk Control. Shockingly, conformal prediction can be used to bound risk functions far beyond coverage. This is a short and useful read (the theory section is 2 pages).

Selected publications
    • A. N. Angelopoulos
    • S. Bates
    • A. Fisch
    • L. Lei
    • T. Schuster
    Conformal Risk Control. 2022.
    • A. N. Angelopoulos*
    • A. P. Kohli*
    • S. Bates
    • J. Malik
    • M. I. Jordan
    • T. Alshaabi
    • S. Upadhyahyula
    • Y. Romano
    Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. 2022.
    • A. N. Angelopoulos
    • S. Bates
    • E. J. Candès
    • M. I. Jordan
    • L. Lei
    Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. 2021.
    • A. N. Angelopoulos
    • S. Bates
    A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification. 2021.
    • S. Bates*
    • A. N. Angelopoulos*
    • L. Lei*
    • J. Malik
    • M. I. Jordan
    Distribution-Free, Risk-Controlling Prediction Sets. 2021.
    • A. N. Angelopoulos*
    • S. Bates*
    • J. Malik
    • M. I. Jordan
    Uncertainty Sets for Image Classifiers using Conformal Prediction. 2020.