52 citations · 418 across the 59 of their papers we have counts for
10 papers · 1 filter
Neural Network Approximation for Pessimistic Offline Reinforcement Learning
Di Wu, Yuling Jiao, Li Shen +2
Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-making scenarios, yet its theoretical guarantees are still under development. Existing wo…
Sampling via Föllmer Flow
Zhao Ding, Yuling Jiao, Xiliang Lu +2
We introduce a novel unit-time ordinary differential equation (ODE) flow called the preconditioned Föllmer flow, which efficiently transforms a Gaussian measure into a desired targ…
Gaussian Interpolation Flows
Yuan Gao, Jian Huang, Yuling Jiao
Gaussian denoising has emerged as a powerful method for constructing simulation-free continuous normalizing flows for generative modeling. Despite their empirical successes, theore…
Lipschitz Transport Maps via the Follmer Flow
Yin Dai, Yuan Gao, Jian Huang +3
Inspired by the construction of the F{ö}llmer process, we construct a unit-time flow on the Euclidean space, termed the F{ö}llmer flow, whose flow map at time 1 pushes forward a st…
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models
Changyu Liu, Yuling Jiao, Junhui Wang +1
We propose a general approach to evaluating the performance of robust estimators based on adversarial losses under misspecified models. We first show that adversarial risk is equiv…
Current density impedance imaging with PINNs
Chenguang Duan, Yuling Jiao, Xiliang Lu +1
In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization. This method constructs a physic…
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We show that the partial derivatives of RePU neural networks can be re…
Deep Neural Network Approximation of Composition Functions: with application to PINNs
Chenguang Duan, Yuling Jiao, Xiliang Lu +2
In this paper, we focus on approximating a natural class of functions that are compositions of smooth functions. Unlike the low-dimensional support assumption on the covariate, we…
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
Yuling Jiao, Di Li, Xiliang Lu +2
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differentia…
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
Yuling Jiao, Yanming Lai, Yang Wang +2
This paper analyzes the convergence rate of a deep Galerkin method for the weak solution (DGMW) of second-order elliptic partial differential equations on with Diric…