Search Results for author:
Found 16 papers, 14 papers with code
Frequency and cardinality recovery from sketched data: a novel approach bridging Bayesian and frequentist views
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.
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, enabling 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.
