Chenghua Guo

Chao Chen, Chenghua Guo, Rufeng Chen et al.

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the $\ell_p$ distance for stability assessment, which diverges from human perception. Besides, existing adversarial training (AT) associated with intensive computations may lead to an arms race. To address these challenges, we introduce a novel metric to assess the stability of top-$k$ salient features. We introduce R2ET which trains for stable explanation by efficient and effective regularizer, and analyze R2ET by multi-objective optimization to prove numerical and statistical stability of explanations. Moreover, theoretical connections between R2ET and certified robustness justify R2ET's stability in all attacks. Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods.

Chao Chen, Chenghua Guo, Guixiang Ma et al.

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.

Chao Chen, Chenghua Guo, Rui Xu et al.

Graphical models have demonstrated their exceptional capabilities across numerous applications, such as social networks, citation networks, and online recommendation systems. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data and the challenges of accurately modeling real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we comprehensively examine existing works on UQ for graphical models, focusing on key aspects such as the sources, representation, handling, and evaluation of uncertainty. This survey distinguishes itself from most existing UQ surveys by specifically concentrating on UQ in graphical models, including probabilistic graphical models (PGMs) and graph neural networks (GNNs). After reviewing sources of uncertainty, we organize the work using two high-level dimensions: uncertainty representation and uncertainty handling. By offering a comprehensive overview of the current landscape, including both established methodologies and emerging trends, we aim to bridge gaps in understanding key challenges and opportunities in UQ for graphical models, hoping to inspire researchers working on graphical models or uncertainty quantification to make further advancements at the cross of the two fields.