Search Results for author:
Found 23 papers, 20 papers with code
Robust Conformal Outlier Detection under Contaminated Reference Data
In outlier detection, this calibration relies on a reference set of labeled inlier data to control the type-I error rate.
Noise-Adaptive Conformal Classification with Marginal Coverage
Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage guarantees for any classification model.
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for type-I censoring.
Conformal Classification with Equalized Coverage for Adaptively Selected Groups
This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features.
Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
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 from the same column.
Uncertainty in Language Models: Assessment through Rank-Calibration
Language Models (LMs) have shown promising performance in natural language generation.
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
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 probability.
A smoothed-Bayesian approach to frequency recovery from sketched data
We provide a novel statistical perspective on a classical problem at the intersection of computer science and information theory: recovering the empirical frequency of a symbol in a large discrete dataset using only a compressed representation, or sketch, obtained via random hashing.
Adaptive conformal classification with noisy labels
This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, leading to more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches.
Derandomized Novelty Detection with FDR Control via Conformal E-values
Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection.
Conformal inference is (almost) free for neural networks trained with early stopping
Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks.
Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries
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.
Bayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data
The estimation of coverage probabilities, and in particular of the missing mass, is a classical statistical problem with applications in numerous scientific fields.
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set.
Coordinated Double Machine Learning
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.
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
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.
Conformal Frequency Estimation with Sketched Data
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.
Conformal Prediction using Conditional Histograms
This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data.
Testing for Outliers with Conformal p-values
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.
Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets
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.
Classification with Valid and Adaptive Coverage
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.
A comparison of some conformal quantile regression methods
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).
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
