11 citations · 34 across the 15 of their papers we have counts for
16 papers
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
Shuai Huang, Zhe Qu, Zhaowei Hua +3
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation…
Balancing Multimodal Domain Generalization via Gradient Modulation and Projection
Hongzhao Li, Guohao Shen, Shupan Li +2
Multimodal Domain Generalization (MMDG) leverages the complementary strengths of multiple modalities to enhance model generalization on unseen domains. A central challenge in multi…
Conformal Set-based Human-AI Complementarity with Multiple Experts
Helbert Paat, Guohao Shen
Decision support systems are designed to assist human experts in classification tasks by providing conformal prediction sets derived from a pre-trained model. This human-AI collabo…
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Yuanhang Luo, Yeheng Ge, Ruijian Han +1
In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep re…
Deep Distributional Learning with Non-crossing Quantile Network
Guohao Shen, Runpeng Dai, Guojun Wu +3
In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that…
Conditional Stochastic Interpolation for Generative Learning
Ding Huang, Jian Huang, Ting Li +1
We propose a conditional stochastic interpolation (CSI) method for learning conditional distributions. CSI is based on estimating probability flow equations or stochastic different…
Deep Neural Newsvendor
Jinhui Han, Ming Hu, Guohao Shen
We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target quantil…
Wasserstein Generative Regression
Shanshan Song, Tong Wang, Guohao Shen +2
In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function…
Exploring the Complexity of Deep Neural Networks through Functional Equivalence
Guohao Shen
We investigate the complexity of deep neural networks through the lens of functional equivalence, which posits that different parameterizations can yield the same network function.…
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…
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction
Wenlu Tang, Guohao Shen, Yuanyuan Lin +1
We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is…
Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +2
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neura…
Deep Quantile Regression: Mitigating the Curse of Dimensionality Through Composition
Guohao Shen, Yuling Jiao, Yuanyuan Lin +2
This paper considers the problem of nonparametric quantile regression under the assumption that the target conditional quantile function is a composition of a sequence of low-dimen…
Robust Nonparametric Regression with Deep Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
In this paper, we study the properties of robust nonparametric estimation using deep neural networks for regression models with heavy tailed error distributions. We establish the n…
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
In this paper, we consider the problem of binary classification with a class of general deep convolutional neural networks, which includes fully-connected neural networks and fully…
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors
Yuling Jiao, Guohao Shen, Yuanyuan Lin +1
We study the properties of nonparametric least squares regression using deep neural networks. We derive non-asymptotic upper bounds for the prediction error of the empirical risk m…