2 citations · 6 across the 17 of their papers we have counts for
17 papers
Diffusion Based Unpaired Data Learning for Inverse Problems
Chenglong Bao, Yiming Dang, Chenguang Duan +2
Data is important in many deep learning-based inverse problem solvers. However, obtaining sufficient paired data in many scenarios remains highly challenging, while unpaired data i…
Approximation Error Upper and Lower Bounds for Hölder Class with Transformers
Xin He, Yuling Jiao, Xiliang Lu +1
We explore the expressive power of Transformers by establishing precise approximation error upper and lower bounds for Hölder class. Specifically, a new approximation upper bound i…
Inference-Time Alignment for Diffusion Models via Variationally Stable Doob's Matching
Jinyuan Chang, Chenguang Duan, Yuling Jiao +2
Inference-time alignment for diffusion models aims to adapt a pre-trained reference diffusion model toward a target distribution without retraining the reference score network, the…
Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
Chenguang Duan, Yuling Jiao, Gabriele Steidl +3
We propose a novel method for sampling from unnormalized Boltzmann densities based on a probability flow ordinary differential equation (ODE) derived from linear stochastic interpo…
Provable Diffusion Posterior Sampling for Bayesian Inversion
Jinyuan Chang, Chenguang Duan, Yuling Jiao +3
We propose a novel diffusion-based posterior sampling method within a plug-and-play framework. Our approach constructs a probability transport from an easy-to-sample distribution t…
Near-optimal Prediction Error Estimation for Quantum Machine Learning Models
Qiuhao Chen, Yuling Jiao, Yinan Li +2
Understanding the theoretical capabilities and limitations of quantum machine learning (QML) models to solve machine learning tasks is crucial to advancing both quantum software an…
Online Quantum State Tomography via Stochastic Gradient Descent
Jian-Feng Cai, Yuling Jiao, Yinan Li +3
We initiate the study of online quantum state tomography (QST), where the matrix representation of an unknown quantum state is reconstructed by sequentially performing a batch of m…
CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation
Ningxin Gui, Qianghuai Jia, Feijun Jiang +3
We introduce CRPE (Code Reasoning Process Enhancer), an innovative three-stage framework for data synthesis and model training that advances the development of sophisticated code r…
Nonlinear Assimilation via Score-based Sequential Langevin Sampling
Zhao Ding, Chenguang Duan, Yuling Jiao +3
This paper introduces score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed…
Deep Transfer Learning: Model Framework and Error Analysis
Yuling Jiao, Huazhen Lin, Yuchen Luo +1
This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples to a single-domain…
Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
Chenguang Duan, Yuling Jiao, Huazhen Lin +2
Learning transferable data representations from abundant unlabeled data remains a central challenge in machine learning. Although numerous self-supervised learning methods have bee…
DRM Revisited: A Complete Error Analysis
Yuling Jiao, Ruoxuan Li, Peiying Wu +2
In this work, we address a foundational question in the theoretical analysis of the Deep Ritz Method (DRM) under the over-parameteriztion regime: Given a target precision level, ho…
Characteristic Learning for Provable One Step Generation
Zhao Ding, Chenguang Duan, Yuling Jiao +3
We propose the characteristic generator, an one-step generative model that combines the sampling efficiency of generative adversarial networks (GANs) with the training stability of…
A Stabilized Physics Informed Neural Networks Method for Wave Equations
Yuling Jiao, Yuhui Liu, Jerry Zhijian Yang +1
In this article, we propose a novel Stabilized Physics Informed Neural Networks method (SPINNs) for solving wave equations. In general, this method not only demonstrates theoretica…
Deep conditional distribution learning via conditional Föllmer flow
Jinyuan Chang, Zhao Ding, Yuling Jiao +2
We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional Föllmer Flow. Starting from a standard G…
Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
Zhao Ding, Chenguang Duan, Yuling Jiao +1
We propose SDORE, a Semi-supervised Deep Sobolev Regressor, for the nonparametric estimation of the underlying regression function and its gradient. SDORE employs deep ReQU neural…
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Zhan Yu, Qiuhao Chen, Yuling Jiao +4
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learn…