490 citations · 3.5k across the 123 of their papers we have counts for
28 papers · 1 filter
Forest Guided Smoothing
Isabella Verdinelli, Larry Wasserman
We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest…
The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder +2
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the…
Cautious Deep Learning
Yotam Hechtlinger, Barnabás Póczos, Larry Wasserman
Most classifiers operate by selecting the maximum of an estimate of the conditional distribution where stands for the features of the instance to be classified and …
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review
Sivaraman Balakrishnan, Larry Wasserman
The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently develope…
Minimax Lower Bounds for Linear Independence Testing
Aaditya Ramdas, David Isenberg, Aarti Singh +1
Linear independence testing is a fundamental information-theoretic and statistical problem that can be posed as follows: given points from a dimen…
An Analysis of Active Learning With Uniform Feature Noise
Aaditya Ramdas, Barnabas Poczos, Aarti Singh +1
In active learning, the user sequentially chooses values for feature and an oracle returns the corresponding label . In this paper, we consider the effect of feature noise i…
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos +2
We propose and analyze estimators for statistical functionals of one or more distributions under nonparametric assumptions. Our estimators are based on the theory of influence func…
On Estimating Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos +1
We give a comprehensive theoretical characterization of a nonparametric estimator for the divergence between two continuous distributions. We first bound the rate of conver…
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions
Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos +2
This paper is about two related decision theoretic problems, nonparametric two-sample testing and independence testing. There is a belief that two recently proposed solutions, base…
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos +1
We consider nonparametric estimation of , Renyi- and Tsallis- divergences between continuous distributions. Our approach is to construct estimators for particular integr…
Tight Lower Bounds for Homology Inference
Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh +1
The homology groups of a manifold are important topological invariants that provide an algebraic summary of the manifold. These groups contain rich topological information, for ins…
Cluster Trees on Manifolds
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo +2
In this paper we investigate the problem of estimating the cluster tree for a density supported on or near a smooth -dimensional manifold isometrically embedded in $\mat…
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
Martin Azizyan, Aarti Singh, Larry Wasserman
While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance…
A Conformal Prediction Approach to Explore Functional Data
Jing Lei, Alessandro Rinaldo, Larry Wasserman
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and…
Distribution-Free Distribution Regression
Barnabas Poczos, Alessandro Rinaldo, Aarti Singh +1
`Distribution regression' refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y=f(P) + mu where f is an unknown…
Differential Privacy for Functions and Functional Data
Rob Hall, Alessandro Rinaldo, Larry Wasserman
Differential privacy is a framework for privately releasing summaries of a database. Previous work has focused mainly on methods for which the output is a finite dimensional vector…
High Dimensional Semiparametric Gaussian Copula Graphical Models
Han Liu, Fang Han, Ming Yuan +2
In this paper, we propose a semiparametric approach, named nonparanormal skeptic, for efficiently and robustly estimating high dimensional undirected graphical models. To achieve m…
Sparse Nonparametric Graphical Models
John Lafferty, Han Liu, Larry Wasserman
We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already esse…
Minimax Rates for Homology Inference
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy +2
Often, high dimensional data lie close to a low-dimensional submanifold and it is of interest to understand the geometry of these submanifolds. The homology groups of a manifold ar…
Stability of Density-Based Clustering
Alessandro Rinaldo, Aarti Singh, Rebecca Nugent +1
High density clusters can be characterized by the connected components of a level set of the underlying probability density function generating the data…
Minimax Manifold Estimation
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We find the minimax rate of convergence in Hausdorff distance for estimating a manifold M of dimension d embedded in R^D given a noisy sample from the manifold. We assume that the…
Graph-Valued Regression
Han Liu, Xi Chen, John Lafferty +1
Undirected graphical models encode in a graph the dependency structure of a random vector . In many applications, it is of interest to model given another random vector…
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
Han Liu, Kathryn Roeder, Larry Wasserman
A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include -fold cr…
Forest Density Estimation
Han Liu, Min Xu, Haijie Gu +3
We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estima…