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Yongkang Zhou

3 accepted papers

2026

SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation

AAAI 2026technical

Feature caching has recently emerged as a promising method for diffusion model acceleration. It effectively alleviates the inefficiency problem caused by high computational requirements by caching similar features in the inference process of the diffusion model. In this paper, we analyze existing fe

Cited by 0SourcePDFScholar
2026

SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning

ICML 2026poster

Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values. Recently, it has been applied to accelerate Vision-Language-Action (VLA) model inference. However, existing acceleration methods focus on local information from the current action step…

Cited by 0SourceScholar
2024

Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections

NeurIPS 2024poster

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks. However, all existing fairness attacks require manipulating the connectivity between e…