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no code implementations • 12 Feb 2024 • Amit Kohli, Anastasios N. Angelopoulos, Laura Waller

The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image.

1 code implementation • 2 Feb 2024 • Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates

We introduce a method for online conformal prediction with decaying step sizes.

1 code implementation • 2 Nov 2023 • Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions.

no code implementations • 9 Oct 2023 • Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions.

1 code implementation • 31 Jul 2023 • Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani

We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees.

1 code implementation • NeurIPS 2023 • Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani

Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-specified probability.

2 code implementations • 23 Jan 2023 • Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic

Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.

no code implementations • 28 Sep 2022 • Shai Feldman, Bat-Sheva Einbinder, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano

In such cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.

1 code implementation • 4 Aug 2022 • Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, Tal Schuster

We extend conformal prediction to control the expected value of any monotone loss function.

1 code implementation • 20 Jul 2022 • Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola

Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street.

1 code implementation • 5 Jul 2022 • Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz

Our work applies these new uncertainty quantification methods -- specifically conformal prediction -- to a deep-learning model for grading the severity of spinal stenosis in lumbar spine MRI.

no code implementations • 4 Jul 2022 • Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan

Building from a pre-trained ranking model, we show how to return a set of items that is rigorously guaranteed to contain mostly good items.

1 code implementation • 8 Feb 2022 • Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan

This is challenging because of a characteristic type of distribution shift between the training and test data in the design setting -- one in which the training and test data are statistically dependent, as the latter is chosen based on the former.

1 code implementation • 3 Oct 2021 • Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, Lihua Lei

We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees.

4 code implementations • 15 Jul 2021 • Anastasios N. Angelopoulos, Stephen Bates

Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models.

1 code implementation • 11 Feb 2021 • Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan

Our method follows the general approach of split conformal prediction; we use holdout data to calibrate the size of the prediction sets but preserve privacy by using a privatized quantile subroutine.

1 code implementation • 7 Apr 2020 • Anastasios N. Angelopoulos, Julien N. P. Martel, Amit P. S. Kohli, Jorg Conradt, Gordon Wetzstein

The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically.

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