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Anastasios Angelopoulos

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3 papers

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NeurIPS Conference 2023 Conference Paper

Class-Conditional Conformal Prediction with Many Classes

Tiffany Ding
Anastasios Angelopoulos
Stephen Bates
Michael 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. In many classificationproblems, we would like to obtain a stronger guarantee--that for test pointsof a specific class, the prediction set contains the true label with thesame user-chosen probability. For the latter goal, existing conformal predictionmethods do not work well when there is a limited amount of labeled data perclass, as is often the case in real applications where the number of classes islarge. We propose a method called clustered conformal prediction thatclusters together classes having "similar" conformal scores and performs conformal prediction at the cluster level. Based on empirical evaluation acrossfour image data sets with many (up to 1000) classes, we find that clusteredconformal typically outperforms existing methods in terms of class-conditionalcoverage and set size metrics.

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NeurIPS Conference 2023 Conference Paper

Conformal PID Control for Time Series Prediction

Anastasios Angelopoulos
Emmanuel 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. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. Our theory both simplifies and strengthens existing analyses in online conformal prediction. Experiments on 4-week-ahead forecasting of statewide COVID-19 death counts in the U. S. show an improvement in coverage over the ensemble forecaster used inofficial CDC communications. We also run experiments on predicting electricity demand, market returns, and temperature using autoregressive, Theta, Prophet, and Transformer models. We provide an extendable codebase for testing our methods and for the integration of new algorithms, data sets, and forecasting rules at this link.

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NeurIPS Conference 2022 Conference Paper

Semantic uncertainty intervals for disentangled latent spaces

Swami Sankaranarayanan
Anastasios 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. To this end, recent breakthroughs in generative modeling allow us to represent semantic information in disentangled latent spaces, but providing uncertainties on the semantic latent variables has remained challenging. In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. The method does the following: (1) it uses quantile regression to output a heuristic uncertainty interval for each element in the latent space (2) calibrates these uncertainties such that they contain the true value of the latent for a new, unseen input. The endpoints of these calibrated intervals can then be propagated through the generator to produce interpretable uncertainty visualizations for each semantic factor. This technique reliably communicates semantically meaningful, principled, and instance-adaptive uncertainty in inverse problems like image super-resolution and image completion. Project page: https: //swamiviv. github. io/semantic uncertainty intervals/

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