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Hua Chen

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8

ICML Conference 2025 Conference Paper

DiffAdvMAP: Flexible Diffusion-Based Framework for Generating Natural Unrestricted Adversarial Examples

  • Zhengzhao Pan
  • Hua Chen
  • Xiaogang Zhang

Unrestricted adversarial examples(UAEs) have posed greater threats to deep neural networks(DNNs) than perturbation-based adversarial examples(AEs) because they can make extensive changes to images without being restricted in a fixed norm perturbation budget. Although current diffusion-based methods can generate more natural UAEs than other unrestricted attack methods, the overall effectiveness of such methods is restricted since they are designed for specific attack conditions. Additionally, the naturalness of UAEs still has room for improvement, as these methods primarily focus on leveraging diffusion models as strong priors to enhance the generation process. This paper proposes a flexible framework named Diffusion-based Adversarial Maximum a Posterior(DiffAdvMAP) to generate more natural UAEs for various scenarios. DiffAdvMAP approaches the generation of UAEs by sampling images from posterior distributions, which is achieved by approximating the posterior distribution of UAEs using the prior distribution of real data learned by the diffusion model. This process enhances the naturalness of the UAEs. By incorporating an adversarial constraint to ensure the effectiveness of the attack, DiffAdvMAP exhibits excellent attack ability and defense robustness. A reconstruction constraint is designed to enhance its flexibility, which allows DiffAdvMAP to be tailored to various attack scenarios. Experimental results on Imagenet show that we achieve a better trade-off between image quality, flexibility, and transferability than baseline unrestricted adversarial attack methods.

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AAAI Conference 2019 Conference Paper

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features

  • Yao Zhang
  • Wen-Ping Fan
  • Xuan Wu
  • Hua Chen
  • Bin-Yang Li
  • Min-Ling Zhang

Virtual desktop infrastructure (VDI) is a virtualization technology that hosts desktop operating system on centralized server in a data center of private or public cloud. Effective resource management is of crucial importance for VDI customers, where maintaining sufficient virtual machines helps guarantee satisfactory user experience while turning off spare virtual machines helps save running cost. Generally, existing techniques work in passive manner by either driving available capacity reactively or configuring management schedules manually. In this paper, a novel proactive resource management approach is proposed which aims to predict VDI pool workload adaptively by utilizing CoArse to Fine historical dEscriptive (CAFE) features. Specifically, aggregate session count from pool end users serves as the basis for workload measurement and predictive model induction. Extensive experiments on real VDI customers data sets clearly validate the effectiveness of multi-grained features for VDI workload prediction. Furthermore, practical insights identified in our VDI data analytics are also discussed.

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TIST Journal 2015 Journal Article

Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes

  • Hua Chen
  • Peng Ding
  • Zhi Geng
  • Xiao-Hua Zhou

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this article, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.

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