
TwoSample Testing on Ranked Preference Data and the Role of Modeling Assumptions
A number of applications require twosample testing on ranked preference...
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Discussion of "On nearly assumptionfree tests of nominal confidence interval coverage for causal parameters estimated by machine learning"
We congratulate the authors on their exciting paper, which introduces a ...
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Minimax optimality of permutation tests
Permutation tests are widely used in statistics, providing a finitesamp...
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A Unified View of Label Shift Estimation
Label shift describes the setting where although the label distribution ...
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Minimax Optimal Conditional Independence Testing
We consider the problem of conditional independence testing of X and Y g...
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Universal Inference Using the Split Likelihood Ratio Test
We propose a general method for constructing hypothesis tests and confid...
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Local Spectral Clustering of Density Upper Level Sets
We analyze the Personalized PageRank (PPR) algorithm, a local spectral m...
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Gaussian Mixture Clustering Using Relative Tests of Fit
We consider clustering based on significance tests for Gaussian Mixture ...
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Minimax Confidence Intervals for the Sliced Wasserstein Distance
The Wasserstein distance has risen in popularity in the statistics and m...
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Interactive Martingale Tests for the Global Null
Global null testing is a classical problem going back about a century to...
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Path Length Bounds for Gradient Descent and Flow
We provide path length bounds on gradient descent (GD) and flow (GF) cur...
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A Unified Approach to Robust Mean Estimation
In this paper, we develop connections between two seemingly disparate, b...
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Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In supervised learning, we leverage a labeled dataset to design methods ...
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Robust Nonparametric Regression under Huber's εcontamination Model
We consider the nonparametric regression problem under Huber's ϵcontam...
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How Many Samples are Needed to Learn a Convolutional Neural Network?
A widespread folklore for explaining the success of convolutional neural...
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Local White Matter Architecture Defines Functional Brain Dynamics
Large bundles of myelinated axons, called white matter, anatomically con...
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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
We consider the problem of global optimization of an unknown nonconvex ...
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Robust Multivariate Nonparametric Tests via ProjectionPursuit
In this work, we generalize the Cramérvon Mises statistic via projectio...
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Robust Estimation via Robust Gradient Estimation
We provide a new computationallyefficient class of estimators for risk ...
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Sharp instruments for classifying compliers and generalizing causal effects
It is wellknown that, without restricting treatment effect heterogeneit...
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Hypothesis Testing for HighDimensional Multinomials: A Selective Review
The statistical analysis of discrete data has been the subject of extens...
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Stochastic Zerothorder Optimization in High Dimensions
We consider the problem of optimizing a highdimensional convex function...
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Low Permutationrank Matrices: Structural Properties and Noisy Completion
We consider the problem of noisy matrix completion, in which the goal is...
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Hypothesis Testing For Densities and HighDimensional Multinomials: Sharp Local Minimax Rates
We consider the goodnessoffit testing problem of distinguishing whethe...
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Computationally Efficient Robust Estimation of Sparse Functionals
Many conventional statistical procedures are extremely sensitive to seem...
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Rate Optimal Estimation and Confidence Intervals for Highdimensional Regression with Missing Covariates
Although a majority of the theoretical literature in highdimensional st...
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Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
We provide two fundamental results on the population (infinitesample) l...
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A Permutationbased Model for Crowd Labeling: Optimal Estimation and Robustness
The aggregation and denoising of crowd labeled data is a task that has g...
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ArbitrageFree Combinatorial Market Making via Integer Programming
We present a new combinatorial market maker that operates arbitragefree...
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Statistical Inference for Cluster Trees
A cluster tree provides a highlyinterpretable summary of a density func...
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Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
We study methods for aggregating pairwise comparison data in order to es...
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Statistical and Computational Guarantees for the BaumWelch Algorithm
The Hidden Markov Model (HMM) is one of the mainstays of statistical mod...
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Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
There are various parametric models for analyzing pairwise comparison da...
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Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Data in the form of pairwise comparisons arises in many domains, includi...
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Statistical guarantees for the EM algorithm: From population to samplebased analysis
We develop a general framework for proving rigorous guarantees on the pe...
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When is it Better to Compare than to Score?
When eliciting judgements from humans for an unknown quantity, one often...
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Tight Lower Bounds for Homology Inference
The homology groups of a manifold are important topological invariants t...
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Cluster Trees on Manifolds
In this paper we investigate the problem of estimating the cluster tree ...
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Recovering Blockstructured Activations Using Compressive Measurements
We consider the problems of detection and localization of a contiguous b...
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Efficient Active Algorithms for Hierarchical Clustering
Advances in sensing technologies and the growth of the internet have res...
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Sparse Additive Functional and Kernel CCA
Canonical Correlation Analysis (CCA) is a classical tool for finding cor...
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Minimax Rates for Homology Inference
Often, high dimensional data lie close to a lowdimensional submanifold ...
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Sivaraman Balakrishnan
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Assistant Professor in the Department of Statistics at Carnegie Mellon University, Faculty Member of the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, Postdoctoral researcher in the Department of Statistics, UC Berkeley, PhD student in the Language Technologies Institute (a part of the School of Computer Science) at Carnegie Mellon University, Member of the CMU Topological Statistics group.