10 citations · 17 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…