Search Results for author: Anastasios N. Angelopoulos

Found 17 papers, 13 papers with code

Wavefront Randomization Improves Deconvolution

no code implementations12 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.

PPI++: Efficient Prediction-Powered Inference

1 code implementation2 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.

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

no code implementations9 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.

Conformal Prediction Motion Planning

Conformal PID Control for Time Series Prediction

1 code implementation31 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.

Conformal Prediction Time Series +2

Class-Conditional Conformal Prediction with Many Classes

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.

Conformal Prediction Test

Prediction-Powered Inference

2 code implementations23 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.

Astronomy regression +1

Conformal Prediction is Robust to Dispersive Label Noise

no code implementations28 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.

Conformal Prediction regression +1

Conformal Risk Control

1 code implementation4 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.

Conformal Prediction

Semantic uncertainty intervals for disentangled latent spaces

1 code implementation20 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.

Image Super-Resolution Uncertainty Quantification

Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets

1 code implementation5 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.

Conformal Prediction Prediction Intervals +2

Recommendation Systems with Distribution-Free Reliability Guarantees

no code implementations4 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.

Learning-To-Rank Recommendation Systems

Conformal prediction for the design problem

1 code implementation8 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.

Conformal Prediction Test

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

1 code implementation3 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.

BIG-bench Machine Learning Instance Segmentation +4

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

4 code implementations15 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.

Conformal Prediction Out-of-Distribution Detection +2

Private Prediction Sets

1 code implementation11 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.

Conformal Prediction Decision Making +1

Event Based, Near Eye Gaze Tracking Beyond 10,000Hz

1 code implementation7 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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