16 citations · 87 across the 30 of their papers we have counts for
10 papers · 1 filter
Semi-Supervised Conditional Diffusion via Label Augmentation
Jin Su, Yuan Gao, Yong Zhou +1
Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data. In practice, however, acquiring high-qu…
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…
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…
Deep Sufficient Representation Learning via Mutual Information
Siming Zheng, Yuanyuan Lin, Jian Huang
We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approxi…
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…
Relative Entropy Gradient Sampler for Unnormalized Distributions
Xingdong Feng, Yuan Gao, Jian Huang +2
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms i…
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Jian Huang, Yuling Jiao, Lican Kang +3
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size. In this paper, we develop a support detect…
On Newton Screening
Jian Huang, Yuling Jiao, Lican Kang +4
Screening and working set techniques are important approaches to reducing the size of an optimization problem. They have been widely used in accelerating first-order methods for so…
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
Jian Huang, Yuling Jiao, Xiliang Lu +2
We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression. SNAP is derived from a suitable form…