
Gaussian discrepancy: a probabilistic relaxation of vector balancing
We introduce a novel relaxation of combinatorial discrepancy called Gaus...
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An Optimal Transport Approach to Causal Inference
We propose a method based on optimal transport theory for causal inferen...
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MultiReference Alignment for sparse signals, Uniform Uncertainty Principles and the Beltway Problem
Motivated by cuttingedge applications like cryoelectron microscopy (cr...
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Rejection sampling from shapeconstrained distributions in sublinear time
We consider the task of generating exact samples from a target distribut...
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The query complexity of sampling from strongly logconcave distributions in one dimension
We establish the first tight lower bound of Ω(loglogκ) on the query comp...
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Optimal dimension dependence of the MetropolisAdjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diff...
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Efficient Interpolation of Density Estimators
We study the problem of space and time efficient evaluation of a nonpara...
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A Statistical Perspective on Coreset Density Estimation
Coresets have emerged as a powerful tool to summarize data by selecting ...
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Fast and Smooth Interpolation on Wasserstein Space
We propose a new method for smoothly interpolating probability measures ...
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Optimal Rates for Estimation of TwoDimensional Totally Positive Distributions
We study minimax estimation of twodimensional totally positive distribu...
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SVGD as a kernelized Wasserstein gradient flow of the chisquared divergence
Stein Variational Gradient Descent (SVGD), a popular sampling algorithm,...
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Projection to Fairness in Statistical Learning
In the context of regression, we consider the fundamental question of ma...
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Exponential ergodicity of mirrorLangevin diffusions
Motivated by the problem of sampling from illconditioned logconcave di...
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Gradient descent algorithms for BuresWasserstein barycenters
We study first order methods to compute the barycenter of a probability ...
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Balancing Gaussian vectors in high dimension
Motivated by problems in controlled experiments, we study the discrepanc...
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Power analysis of knockoff filters for correlated designs
The knockoff filter introduced by Barber and Candès 2016 is an elegant f...
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Estimation of Wasserstein distances in the Spiked Transport Model
We propose a new statistical model, the spiked transport model, which fo...
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Fast convergence of empirical barycenters in Alexandrov spaces and the Wasserstein space
This work establishes fast rates of convergence for empirical barycenter...
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Estimation Rates for Sparse Linear Cyclic Causal Models
Causal models are important tools to understand complex phenomena and pr...
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Minimax rates of estimation for smooth optimal transport maps
Brenier's theorem is a cornerstone of optimal transport that guarantees ...
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Estimation of Monge Matrices
Monge matrices and their permuted versions known as preMonge matrices n...
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Entropic optimal transport is maximumlikelihood deconvolution
We give a statistical interpretation of entropic optimal transport by sh...
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Uncoupled isotonic regression via minimum Wasserstein deconvolution
Isotonic regression is a standard problem in shapeconstrained estimatio...
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Statistical Optimal Transport via Geodesic Hubs
We propose a new method to estimate Wasserstein distances and optimal tr...
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Sparse Gaussian ICA
Independent component analysis (ICA) is a cornerstone of modern data ana...
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Teacher Improves Learning by Selecting a Training Subset
We call a learner superteachable if a teacher can trim down an iid trai...
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Minimax Rates and Efficient Algorithms for Noisy Sorting
There has been a recent surge of interest in studying permutationbased ...
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Nearlinear time approximation algorithms for optimal transport via Sinkhorn iteration
Computing optimal transport distances such as the earth mover's distance...
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Optimal Rates of Statistical Seriation
Given a matrix the seriation problem consists in permuting its rows in s...
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Online learning in repeated auctions
Motivated by online advertising auctions, we consider repeated Vickrey a...
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Computational Lower Bounds for Sparse PCA
In the context of sparse principal component detection, we bring evidenc...
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Bounded regret in stochastic multiarmed bandits
We study the stochastic multiarmed bandit problem when one knows the va...
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Deviation optimal learning using greedy Qaggregation
Given a finite family of functions, the goal of model selection aggregat...
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Optimal detection of sparse principal components in high dimension
We perform a finite sample analysis of the detection levels for sparse p...
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The multiarmed bandit problem with covariates
We consider a multiarmed bandit problem in a setting where each arm pro...
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NeymanPearson classification, convexity and stochastic constraints
Motivated by problems of anomaly detection, this paper implements the Ne...
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KullbackLeibler aggregation and misspecified generalized linear models
In a regression setup with deterministic design, we study the pure aggre...
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Philippe Rigollet
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Associate Professor at Massachusetts Institute of Technology