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05/24/2023
Weak Signal Detection via Displacement Interpolation
Detecting weak, systematic signals hidden in a large collection of p-val...
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12/05/2022
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
Covariate distribution shifts and adversarial perturbations present robu...
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04/09/2022
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
We study Langevin dynamics for recovering the planted signal in the spik...
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02/09/2022
Online Learning to Transport via the Minimal Selection Principle
Motivated by robust dynamic resource allocation in operations research, ...
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09/28/2021
Reversible Gromov-Monge Sampler for Simulation-Based Inference
This paper introduces a new simulation-based inference procedure to mode...
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03/31/2021
Universal Prediction Band via Semi-Definite Programming
We propose a computationally efficient method to construct nonparametric...
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01/28/2021
Interpolating Classifiers Make Few Mistakes
This paper provides elementary analyses of the regret and generalization...
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10/28/2020
Deep Learning for Individual Heterogeneity
We propose a methodology for effectively modeling individual heterogenei...
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04/09/2020
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
In this paper, we utilize a connection between compositional kernels and...
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02/05/2020
A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers
This paper establishes a precise high-dimensional asymptotic theory for ...
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11/02/2019
Estimating Certain Integral Probability Metric (IPM) is as Hard as Estimating under the IPM
We study the minimax optimal rates for estimating a range of Integral Pr...
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08/27/2019
On the Minimax Optimality of Estimating the Wasserstein Metric
We study the minimax optimal rate for estimating the Wasserstein-1 metri...
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08/27/2019
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
We study the risk of minimum-norm interpolants of data in a Reproducing ...
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01/21/2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
Consider the problem: given data pair (x, y) drawn from a population wit...
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11/07/2018
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results
We study in this paper the rate of convergence for learning distribution...
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09/26/2018
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands
We study deep neural networks and their use in semiparametric inference....
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08/01/2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
In the absence of explicit regularization, Kernel "Ridgeless" Regression...
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02/18/2018
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
We study the detailed path-wise behavior of the discrete-time Langevin a...
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02/16/2018
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Motivated by the pursuit of a systematic computational and algorithmic u...
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12/21/2017
How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View
We study in this paper the rate of convergence for learning densities un...
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12/21/2017
How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View
We study in this paper the rate of convergence for learning densities un...
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12/20/2017
Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
Modern statistical inference tasks often require iterative optimization ...
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11/05/2017
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
We study the relationship between geometry and capacity measures for dee...
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09/12/2017
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
We study the misclassification error for community detection in general ...
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03/22/2016
Inference via Message Passing on Partially Labeled Stochastic Block Models
We study the community detection and recovery problem in partially-label...
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02/21/2015
Learning with Square Loss: Localization through Offset Rademacher Complexity
We consider regression with square loss and general classes of functions...
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02/06/2015
Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix
The interplay between computational efficiency and statistical accuracy ...
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04/17/2014
Geometric Inference for General High-Dimensional Linear Inverse Problems
This paper presents a unified geometric framework for the statistical an...
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02/11/2014