3 citations · 6 across the 10 of their papers we have counts for
10 papers
Towards One-for-All Robustness Across a Continuum of Threat Levels
Zhichao Hou, Xiaorui Liu
Adversarially robust models often overfit to a specific attack budget, necessitating multiple specialized models for diverse and dynamic adversarial environments, a strategy that b…
Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving
Jiewen Liu, Rui Liu, Matthew Lee +3
Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collab…
Fine-Grained Iterative Adversarial Attacks with Limited Computation Budget
Zhichao Hou, Weizhi Gao, Xiaorui Liu
This work tackles a critical challenge in AI safety research under limited compute: given a fixed computation budget, how can one maximize the strength of iterative adversarial att…
Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
Weizhi Gao, Xiaorui Liu, Feiyi Wang +2
Large language models (LLMs) have demonstrated impressive performance in both research and real-world applications, but they still struggle with hallucination. Existing hallucinati…
Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
Weizhi Gao, Zhichao Hou, Junqi Yin +3
Diffusion models have emerged as powerful generative models, but their high computation cost in iterative sampling remains a significant bottleneck. In this work, we present an in-…
Boosting Adversarial Robustness and Generalization with Dictionary Structure
Zhichao Hou, Weizhi Gao, Hamid Krim +2
This work investigates a novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models. Specifically,…
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing
Weizhi Gao, Zhichao Hou, Han Xu +1
Implicit models such as Deep Equilibrium Models (DEQs) have emerged as promising alternative approaches for building deep neural networks. Their certified robustness has gained inc…
ProTransformer: Robustify Transformers via Plug-and-Play Paradigm
Zhichao Hou, Weizhi Gao, Yuchen Shen +2
Transformer-based architectures have dominated various areas of machine learning in recent years. In this paper, we introduce a novel robust attention mechanism designed to enhance…
Robustness Reprogramming for Representation Learning
Zhichao Hou, MohamadAli Torkamani, Hamid Krim +1
This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustnes…
Robust Graph Neural Networks via Unbiased Aggregation
Zhichao Hou, Ruiqi Feng, Tyler Derr +1
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of nume…