Search Results for author: Huiming Zhang

Found 19 papers, 2 papers with code

Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm

no code implementations13 Mar 2023 Huiming Zhang, Haoyu Wei, Guang Cheng

In non-asymptotic statistical inferences, variance-type parameters of sub-Gaussian distributions play a crucial role.

Open-Vocabulary Object Detection With an Open Corpus

no code implementations ICCV 2023 Jiong Wang, Huiming Zhang, Haiwen Hong, Xuan Jin, Yuan He, Hui Xue, Zhou Zhao

For the classification task, we introduce an open corpus classifier by reconstructing original classifier with similar words in text space.

object-detection Open Vocabulary Object Detection

Distribution Estimation of Contaminated Data via DNN-based MoM-GANs

no code implementations28 Dec 2022 Fang Xie, Lihu Xu, Qiuran Yao, Huiming Zhang

This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation.

Inference and FDR Control for Simulated Markov Random Fields in High-dimension

no code implementations11 Feb 2022 Haoyu Wei, Xiaoyu Lei, Huiming Zhang

We further propose a decorrelated score test based on the decorrelated score function and prove the asymptotic normality of the score function without the influence of many nuisance parameters under the assumption that it accelerates the convergence of the MCMC method.

Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption

no code implementations10 Jan 2022 Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang

There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance.


Artistic Style Transfer with Internal-external Learning and Contrastive Learning

1 code implementation NeurIPS 2021 Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu

Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.

Contrastive Learning Style Transfer

Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation

1 code implementation22 Oct 2021 Yang Zhang, Moyun Liu, Huiming Zhang, Guodong Sun, Jingwu He

To reduce time complexity while improving performance, a sparse representation of global nodes based on noise-free online low-rank representation is used to obtain a global graph at each scale.

Density Estimation Image Segmentation +2

DualAST: Dual Style-Learning Networks for Artistic Style Transfer

no code implementations CVPR 2021 Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu

Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.

Style Transfer

Directional FDR Control for Sub-Gaussian Sparse GLMs

no code implementations2 May 2021 Chang Cui, Jinzhu Jia, Yijun Xiao, Huiming Zhang

Using the debiased estimator, we establish multiple testing procedures.

Sharper Sub-Weibull Concentrations

no code implementations4 Feb 2021 Huiming Zhang, Haoyu Wei

Constant-specified and exponential concentration inequalities play an essential role in the finite-sample theory of machine learning and high-dimensional statistics area.

A Unified Light Framework for Real-time Fault Detection of Freight Train Images

no code implementations31 Jan 2021 Yang Zhang, Moyun Liu, Yang Yang, Yanwen Guo, Huiming Zhang

Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements.

Fault Detection Region Proposal

Concentration Inequalities for Statistical Inference

no code implementations4 Nov 2020 Huiming Zhang, Song Xi Chen

This paper gives a review of concentration inequalities which are widely employed in non-asymptotical analyses of mathematical statistics in a wide range of settings, from distribution-free to distribution-dependent, from sub-Gaussian to sub-exponential, sub-Gamma, and sub-Weibull random variables, and from the mean to the maximum concentration.

Non-asymptotic Optimal Prediction Error for Growing-dimensional Partially Functional Linear Models

no code implementations10 Sep 2020 Huiming Zhang, Xiaoyu Lei

From the non-asymptotic point of view, we focus on the rate-optimal upper and lower bounds of the prediction error.

Weighted Lasso Estimates for Sparse Logistic Regression: Non-asymptotic Properties with Measurement Error

no code implementations11 Jun 2020 Huamei Huang, Yujing Gao, Huiming Zhang, Bo Li

When we are interested in high-dimensional system and focus on classification performance, the $\ell_{1}$-penalized logistic regression is becoming important and popular.


Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data

no code implementations21 May 2020 Jun Yu, HaiYing Wang, Mingyao Ai, Huiming Zhang

We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria.

LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching

no code implementations ICLR 2020 Zhiwen Zuo, Lei Zhao, Huiming Zhang, Qihang Mo, Haibo Chen, Zhizhong Wang, Ailin Li, Lihong Qiu, Wei Xing, Dongming Lu

Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.

Sparse Density Estimation with Measurement Errors

no code implementations14 Nov 2019 Xiaowei Yang, Huiming Zhang, Haoyu Wei, Shouzheng Zhang

It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.

Density Estimation

Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection

no code implementations9 Dec 2017 Huiming Zhang, Jinzhu Jia

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator.


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