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no code implementations • 27 Sep 2023 • Mario Beraha, Stefano Favaro, Matteo Sesia

We study how to recover the frequency of a symbol in a large discrete data set, using only a compressed representation, or sketch, of those data obtained via random hashing.

1 code implementation • 10 Sep 2023 • Matteo Sesia, Y. X. Rachel Wang, Xin Tong

This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, enabling more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches.

1 code implementation • NeurIPS 2023 • Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia

Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection.

1 code implementation • 27 Jan 2023 • Ziyi Liang, Yanfei Zhou, Matteo Sesia

Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks.

1 code implementation • 9 Nov 2022 • Matteo Sesia, Stefano Favaro, Edgar Dobriban

This paper develops conformal inference methods to construct a confidence interval for the frequency of a queried object in a very large discrete data set, based on a sketch with a lower memory footprint.

no code implementations • 5 Sep 2022 • Stefano Favaro, Matteo Sesia

The estimation of coverage probabilities, and in particular of the missing mass, is a classical statistical problem with applications in numerous scientific fields.

1 code implementation • 23 Aug 2022 • Ziyi Liang, Matteo Sesia, Wenguang Sun

This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set.

1 code implementation • 2 Jun 2022 • Nitai Fingerhut, Matteo Sesia, Yaniv Romano

Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model.

1 code implementation • 12 May 2022 • Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou

Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities.

1 code implementation • 8 Apr 2022 • Matteo Sesia, Stefano Favaro

A flexible conformal inference method is developed to construct confidence intervals for the frequencies of queried objects in very large data sets, based on a much smaller sketch of those data.

1 code implementation • NeurIPS 2021 • Matteo Sesia, Yaniv Romano

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data.

1 code implementation • 16 Apr 2021 • Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, Matteo Sesia

We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees.

1 code implementation • 8 Jun 2020 • Charmaine Chia, Matteo Sesia, Chi-Sing Ho, Stefanie S. Jeffrey, Jennifer Dionne, Emmanuel J. Candès, Roger T. Howe

Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions.

2 code implementations • NeurIPS 2020 • Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with guaranteed marginal coverage.

1 code implementation • 12 Sep 2019 • Matteo Sesia, Emmanuel J. Candès

We compare two recently proposed methods that combine ideas from conformal inference and quantile regression to produce locally adaptive and marginally valid prediction intervals under sample exchangeability (Romano et al., 2019; Kivaranovic et al., 2019).

4 code implementations • 16 Nov 2018 • Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.

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