92 citations · 151 across the 16 of their papers we have counts for
17 papers
Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling
Joaquin Alvarez, Matteo Sesia, Jyotirmoy V. Deshmukh +1
Uncertainty quantification for continuous-time trajectories is a prerequisite in many safety-critical engineering domains. However, a major challenge in data-driven uncertainty qua…
Audited Conformal Prediction for Classification under Unknown Distribution Shift
Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1
We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift. We propose Audited Conformal Prediction (ACP)…
Robust Conformal Outlier Detection under Contaminated Reference Data
Meshi Bashari, Matteo Sesia, Yaniv Romano
Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibratio…
Noise-Adaptive Conformal Classification with Marginal Coverage
Teresa Bortolotti, Y. X. Rachel Wang, Xin Tong +3
Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage gua…
Conformal Classification with Equalized Coverage for Adaptively Selected Groups
Yanfei Zhou, Matteo Sesia
This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen feat…
Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
Ziyi Liang, Tianmin Xie, Xin Tong +1
We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries…
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
Yanfei Zhou, Lars Lindemann, Matteo Sesia
This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high proba…
Conformal Predictive Programming for Chance Constrained Optimization
Yiqi Zhao, Xinyi Yu, Matteo Sesia +2
We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.e., optimization problems with constraints that are functions of…
Adaptive conformal classification with noisy labels
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, leading to mor…
Derandomized Novelty Detection with FDR Control via Conformal E-values
Meshi Bashari, Amir Epstein, Yaniv Romano +1
Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection. While this approach has…
Conformal inference is (almost) free for neural networks trained with early stopping
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. Models trained…
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
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. Blending inductive and transductive conform…
Coordinated Double Machine Learning
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…
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia +1
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 re…
Conformal Prediction using Conditional Histograms
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. Leveraging black-box machine learn…
Testing for Outliers with Conformal p-values
Stephen Bates, Emmanuel Candès, Lihua Lei +2
This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belo…
Classification with Valid and Adaptive Coverage
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…