8 citations · 22 across the 10 of their papers we have counts for
10 papers
Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations
Riccardo Fogliato, Pratik Patil, Pietro Perona
Matching algorithms are commonly used to predict matches between items in a collection. For example, in 1:1 face verification, a matching algorithm predicts whether two face images…
Generalized equivalences between subsampling and ridge regularization
Pratik Patil, Jin-Hong Du
We establish precise structural and risk equivalences between subsampling and ridge regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic f…
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges…
Extrapolated cross-validation for randomized ensembles
Jin-Hong Du, Pratik Patil, Kathryn Roeder +1
Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of en…
Asymptotics of the Sketched Pseudoinverse
Daniel LeJeune, Pratik Patil, Hamid Javadi +2
We take a random matrix theory approach to random sketching and show an asymptotic first-order equivalence of the regularized sketched pseudoinverse of a positive semidefinite matr…
Bagging in overparameterized learning: Risk characterization and risk monotonization
Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures. In this paper, we study the prediction risk of…
Mitigating multiple descents: A model-agnostic framework for risk monotonization
Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei +1
Recent empirical and theoretical analyses of several commonly used prediction procedures reveal a peculiar risk behavior in high dimensions, referred to as double/multiple descent,…
Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals
Michael Stanley, Pratik Patil, Mikael Kuusela
Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared detector-level data. For computational and practical reaso…
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +3
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State cluste…
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +2
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-n…