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Found 19 papers, 4 papers with code
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data
We propose a weighted rolling-validation procedure, an online variant of leave-one-out cross-validation, that costs minimal extra computation for many typical stochastic gradient descent estimators.
Detecting Errors in a Numerical Response via any Regression Model
Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/processing mistakes, or imperfect human estimates.
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Here we instead investigate a model-independent method that does not make any assumptions about the signal and uses a semi-supervised classifier to detect the presence of the signal in the experimental data.
Applications High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
In this work we study sparse PCA based on the convex FPS formulation, and propose a new algorithm that is computationally efficient and applicable to large and high-dimensional data sets.
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces
We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces.
Cross-Validation with Confidence
Cross-validation is one of the most popular model selection methods in statistics and machine learning.
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label.
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information and mutual information.
Distribution-Free Predictive Inference For Regression
In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
A Minimax Theory for Adaptive Data Analysis
In this paper, we propose a minimax framework for adaptive data analysis.
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Lastly, we extend some of the results to the more practical $(\epsilon,\delta)$-differential privacy and establish the existence of a phase-transition on the class of problems that are approximately privately learnable with respect to how small $\delta$ needs to be.
A Generic Sample Splitting Approach for Refined Community Recovery in Stochastic Block Models
We propose and analyze a generic method for community recovery in stochastic block models and degree corrected block models.
The functional mean-shift algorithm for mode hunting and clustering in infinite dimensions
We introduce the functional mean-shift algorithm, an iterative algorithm for estimating the local modes of a surrogate density from functional data.
Sparsistency and agnostic inference in sparse PCA
What can be said about the results of sparse PCA without assuming a sparse and unique truth?
Consistency of spectral clustering in stochastic block models
We analyze the performance of spectral clustering for community extraction in stochastic block models.
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-$d$ projection matrices (the Fantope).
A Conformal Prediction Approach to Explore Functional Data
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data.
Minimax sparse principal subspace estimation in high dimensions
We study sparse principal components analysis in high dimensions, where $p$ (the number of variables) can be much larger than $n$ (the number of observations), and analyze the problem of estimating the subspace spanned by the principal eigenvectors of the population covariance matrix.
Differentially Private M-Estimators
This paper studies privacy preserving M-estimators using perturbed histograms.
