b'Yuling Jiao'

research

∙ 05/01/2023

Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression

We study the properties of differentiable neural networks activated by r...

0 Guohao Shen, et al. ∙

research

∙ 04/17/2023

Deep Neural Network Approximation of Composition Functions: with application to PINNs

In this paper, we focus on approximating a natural class of functions th...

0 Chenguang Duan, et al. ∙

research

∙ 03/28/2023

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs

With recent study of the deep learning in scientific computation, the PI...

0 Yuling Jiao, et al. ∙

research

∙ 02/05/2023

Convergence Analysis of the Deep Galerkin Method for Weak Solutions

This paper analyzes the convergence rate of a deep Galerkin method for t...

0 Yuling Jiao, et al. ∙

research

∙ 07/21/2022

Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks

We propose a penalized nonparametric approach to estimating the quantile...

0 Guohao Shen, et al. ∙

research

∙ 05/19/2022

Deep Generative Survival Analysis: Nonparametric Estimation of Conditional Survival Function

We propose a deep generative approach to nonparametric estimation of con...

0 Xingyu Zhou, et al. ∙

research

∙ 04/14/2022

Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning

Efficient quantum compiling tactics greatly enhance the capability of qu...

0 Qiuhao Chen, et al. ∙

research

∙ 01/24/2022

Approximation bounds for norm constrained neural networks with applications to regression and GANs

This paper studies the approximation capacity of ReLU neural networks wi...

0 Yuling Jiao, et al. ∙

research

∙ 12/19/2021

Wasserstein Generative Learning of Conditional Distribution

Conditional distribution is a fundamental quantity for describing the re...

19 Shiao Liu, et al. ∙

research

∙ 12/15/2021

Sample-Efficient Sparse Phase Retrieval via Stochastic Alternating Minimization

In this work we propose a nonconvex two-stage stochastic alternating min...

0 Jian-Feng Cai, et al. ∙

research

∙ 11/29/2021

Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension

In this paper, we consider recovering n dimensional signals from m binar...

0 Yuling Jiao, et al. ∙

research

∙ 11/21/2021

A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis

In this work, we consider the algorithm to the (nonlinear) regression pr...

0 Peili Li, et al. ∙

research

∙ 11/03/2021

Analysis of Deep Ritz Methods for Laplace Equations with Dirichlet Boundary Conditions

Deep Ritz methods (DRM) have been proven numerically to be efficient in ...

0 Chenguang Duan, et al. ∙

research

∙ 10/24/2021

Non-Asymptotic Error Bounds for Bidirectional GANs

We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimati...

0 Shiao Liu, et al. ∙

research

∙ 10/19/2021

A Deep Generative Approach to Conditional Sampling

We propose a deep generative approach to sampling from a conditional dis...

0 Xingyu Zhou, et al. ∙

research

∙ 10/06/2021

Relative Entropy Gradient Sampler for Unnormalized Distributions

We propose a relative entropy gradient sampler (REGS) for sampling from ...

2 Xingdong Feng, et al. ∙

research

∙ 09/18/2021

Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly

Recovering sparse signals from observed data is an important topic in si...

0 Yuling Jiao, et al. ∙

research

∙ 09/04/2021

Convergence Analysis for the PINNs

In recent years, physical informed neural networks (PINNs) have been sho...

0 Yuling Jiao, et al. ∙

research

∙ 07/30/2021

Error Analysis of Deep Ritz Methods for Elliptic Equations

Using deep neural networks to solve PDEs has attracted a lot of attentio...

0 Yuling Jiao, et al. ∙

research

∙ 07/21/2021

Robust Nonparametric Regression with Deep Neural Networks

In this paper, we study the properties of robust nonparametric estimatio...

0 Guohao Shen, et al. ∙

research

∙ 07/10/2021

Deep Quantile Regression: Mitigating the Curse of Dimensionality Through Composition

This paper considers the problem of nonparametric quantile regression un...

0 Guohao Shen, et al. ∙

research

∙ 07/10/2021

Convergence Analysis of Schrödinger-Föllmer Sampler without Convexity

Schrödinger-Föllmer sampler (SFS) is a novel and efficient approach for ...

0 Yuling Jiao, et al. ∙

research

∙ 06/21/2021

Schrödinger-Föllmer Sampler: Sampling without Ergodicity

Sampling from probability distributions is an important problem in stati...

0 Jian Huang, et al. ∙

research

∙ 06/19/2021

Deep Generative Learning via Schrödinger Bridge

We propose to learn a generative model via entropy interpolation with a ...

48 Gefei Wang, et al. ∙

research

∙ 05/27/2021

An error analysis of generative adversarial networks for learning distributions

This paper studies how well generative adversarial networks (GANs) learn...

0 Jian Huang, et al. ∙

research

∙ 05/01/2021

Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks

In this paper, we consider the problem of binary classification with a c...

0 Guohao Shen, et al. ∙

research

∙ 04/14/2021

Deep Nonparametric Regression on Approximately Low-dimensional Manifolds

In this paper, we study the properties of nonparametric least squares re...

0 Yuling Jiao, et al. ∙

research

∙ 03/24/2021

Convergence Rate Analysis for Deep Ritz Method

Using deep neural networks to solve PDEs has attracted a lot of attentio...

0 Chenguang Duan, et al. ∙

research

∙ 02/28/2021

Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality on Hölder Class

In this paper, we construct neural networks with ReLU, sine and 2^x as a...

0 Yuling Jiao, et al. ∙

research

∙ 12/11/2020

Generative Learning With Euler Particle Transport

We propose an Euler particle transport (EPT) approach for generative lea...

22 Yuan Gao, et al. ∙

research

∙ 06/10/2020

Deep Dimension Reduction for Supervised Representation Learning

The success of deep supervised learning depends on its automatic data re...

37 Jian Huang, et al. ∙

research

∙ 06/03/2020

Robust Decoding from Binary Measurements with Cardinality Constraint Least Squares

The main goal of 1-bit compressive sampling is to decode n dimensional s...

0 Zhao Ding, et al. ∙

research

∙ 02/09/2020

ℓ_0-Regularized High-dimensional Accelerated Failure Time Model

We develop a constructive approach for ℓ_0-penalized estimation in the s...

0 Xingdong Feng, et al. ∙

research

∙ 02/07/2020

Learning Implicit Generative Models with Theoretical Guarantees

We propose a unified framework for implicit generative modeling (UnifiGe...

0 Yuan Gao, et al. ∙

research

∙ 01/27/2020

On Newton Screening

Screening and working set techniques are important approaches to reducin...

0 Jian Huang, et al. ∙

research

∙ 01/16/2020

A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models

Feature selection is important for modeling high-dimensional data, where...

0 Jian Huang, et al. ∙

research

∙ 06/14/2019

A stochastic alternating minimizing method for sparse phase retrieval

Sparse phase retrieval plays an important role in many fields of applied...

0 Jianfeng Cai, et al. ∙

research

∙ 02/25/2019

Wasserstein-Wasserstein Auto-Encoders

To address the challenges in learning deep generative models (e.g.,the b...

0 Shunkang Zhang, et al. ∙

research

∙ 10/09/2018

SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties

We propose a semismooth Newton algorithm for pathwise optimization (SNAP...

0 Jian Huang, et al. ∙

research

∙ 07/27/2018

VIMCO: Variational Inference for Multiple Correlated Outcomes in Genome-wide Association Studies

In Genome-Wide Association Studies (GWAS) where multiple correlated trai...

0 Xingjie Shi, et al. ∙

research

∙ 05/03/2018

REMI: Regression with marginal information and its application in genome-wide association studies

In this study, we consider the problem of variable selection and estimat...

0 Jian Huang, et al. ∙

research

∙ 02/24/2018

Semi-Smooth Newton Algorithm for Non-Convex Penalized Linear Regression

Both the smoothly clipped absolute deviation (SCAD) and the minimax conc...

0 Yueyong Shi, et al. ∙

research

∙ 11/03/2017

Robust Decoding from 1-Bit Compressive Sampling with Least Squares

In 1-bit compressive sensing (1-bit CS) where target signal is coded int...

0 Jian Huang, et al. ∙

research

∙ 03/03/2014

A Primal Dual Active Set with Continuation Algorithm for the ℓ^0-Regularized Optimization Problem

We develop a primal dual active set with continuation algorithm for solv...

0 Yuling Jiao, et al. ∙

research

∙ 10/04/2013

A Primal Dual Active Set Algorithm for a Class of Nonconvex Sparsity Optimization

In this paper, we consider the problem of recovering a sparse vector fro...

0 Yuling Jiao, et al. ∙

Yuling Jiao

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