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Guihong Wan

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12

IJCAI Conference 2025 Conference Paper

FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models

  • Yujie Lin
  • Dong Li
  • Minglai Shao
  • Guihong Wan
  • Chen Zhao

Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Traditional methods for addressing fairness have failed in domain generalization due to their lack of consideration for distribution shifts. Although disentanglement has been used to tackle FairDG, it is limited by its strong assumptions. To overcome these limitations, we propose Fairness-aware Classifier-Guided Score-based Diffusion Models (FADE) as a novel approach to effectively address the FairDG issue. Specifically, we first pre-train a score-based diffusion model (SDM) and two classifiers to equip the model with strong generalization capabilities across different domains. Then, we guide the SDM using these pre-trained classifiers to effectively eliminate sensitive information from the generated data. Finally, the generated fair data is used to train downstream classifiers, ensuring robust performance under new data distributions. Extensive experiments on three real-world datasets demonstrate that FADE not only enhances fairness but also improves accuracy in the presence of distribution shifts. Additionally, FADE outperforms existing methods in achieving the best accuracy-fairness trade-offs.

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TMLR Journal 2025 Journal Article

Multi-Modal Foundation Models for Computational Pathology: A Survey

  • Dong Li
  • Guihong Wan
  • Xintao Wu
  • Xinyu Wu
  • Xiaohui Chen
  • Yi He
  • Zhong Chen
  • Peter K Sorger

Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data sources such as textual reports, structured domain knowledge, and molecular profiles. In this survey, we provide a comprehensive and up-to-date review of multi-modal foundation models in CPath, with a particular focus on models built upon hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level representations. We categorize 34 state-of-the-art multi-modal foundation models into three major paradigms: vision-language, vision-knowledge graph, and vision-gene expression. We further divide vision-language models into non-LLM-based and LLM-based approaches. Additionally, we analyze 30 available multi-modal datasets tailored for pathology, grouped into image-text pairs, instruction datasets, and image-other modality pairs. Our survey also presents a taxonomy of downstream tasks, highlights training and evaluation strategies, and identifies key challenges and future directions. We aim for this survey to serve as a valuable resource for researchers and practitioners working at the intersection of pathology and AI.

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AAAI Conference 2025 Short Paper

Multi-View Unsupervised Column Subset Selection via Combinatorial Search (Student Abstract)

  • Guihong Wan
  • Ninghui Hao
  • Crystal Maung
  • Haim Schweitzer
  • Chen Zhao
  • Kun-Hsing Yu
  • Yevgeniy R. Semenov

Given a data matrix, unsupervised column subset selection refers to the problem of identifying a subset of columns that can be used to linearly approximate the original data matrix. This problem has many applications, such as feature selection and representative selection, but solving it optimally is known to be NP-hard. We consider multi-view unsupervised column subset selection, which extends the concept of (single-view) column subset selection to data represented in multiple views or modalities. We introduce a combinatorial search algorithm for this generalized problem. One variant of the algorithm is guaranteed to compute an optimal solution in a setting similar to the classical A* algorithm. Other suboptimal variants, in a setting similar to the weighted A* algorithm, are much faster and provide a solution along with a bound on its quality.

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AAAI Conference 2024 Short Paper

Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract)

  • Guihong Wan
  • Wei Mao
  • Yevgeniy R. Semenov
  • Haim Schweitzer

The common criteria for evaluating spectral clustering are NCut and RatioCut. The seemingly unrelated column subset selection (CSS) problem aims to compute a column subset that linearly approximates the entire matrix. A common criterion is the approximation error in the Frobenius norm (ApproxErr). We show that any algorithm for CSS can be viewed as a clustering algorithm that minimizes NCut by applying it to a matrix formed from graph edges. Conversely, any clustering algorithm can be seen as identifying a column subset from that matrix. In both cases, ApproxErr and NCut have the same value. Analogous results hold for RatioCut with a slightly different matrix. Therefore, established results for CSS can be mapped to spectral clustering. We use this to obtain new clustering algorithms, including an optimal one that is similar to A*. This is the first nontrivial clustering algorithm with such an optimality guarantee. A variant of the weighted A* runs much faster and provides bounds on the accuracy. Finally, we use the results from spectral clustering to prove the NP-hardness of CSS from sparse matrices.

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AAAI Conference 2024 Short Paper

Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)

  • Wei Mao
  • Guihong Wan
  • Haim Schweitzer

Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering algorithms.

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AAAI Conference 2024 Short Paper

Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract)

  • Boshen Yan
  • Guihong Wan
  • Haim Schweitzer
  • Zoltan Maliga
  • Sara Khattab
  • Kun-Hsing Yu
  • Peter K. Sorger
  • Yevgeniy R. Semenov

Graph spectral clustering is a fundamental technique in data analysis, which utilizes eigenpairs of the Laplacian matrix to partition graph vertices into clusters. However, classical spectral clustering algorithms require eigendecomposition of the Laplacian matrix, which has cubic time complexity. In this work, we describe pass-efficient spectral clustering algorithms that leverage recent advances in randomized eigendecomposition and the structure of the graph vertex-edge matrix. Furthermore, we derive formulas for their efficient implementation. The resulting algorithms have a linear time complexity with respect to the number of vertices and edges and pass over the graph constant times, making them suitable for processing large graphs stored on slow memory. Experiments validate the accuracy and efficiency of the algorithms.

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AAAI Conference 2023 Conference Paper

Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality

  • Guihong Wan
  • Meng Jiao
  • Xinglong Ju
  • Yu Zhang
  • Haim Schweitzer
  • Feng Liu

Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A* to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.

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AAAI Conference 2021 Short Paper

A New Robust Subspace Recovery Algorithm (Student Abstract)

  • Guihong Wan
  • Haim Schweitzer

A common task in data analysis is to compute an approximate embedding of the data in a low dimensional subspace. Robust Subspace Recovery computes the embedding by ignoring a fraction of the data considered as outliers. Its performance can be evaluated by how accurate the inliers are represented. We propose a new algorithm that outperforms the current state of the art when the data is dominated by outliers. The main idea is to rank each point by evaluating the change in the global PCA error when that point is considered as an outlier. We show that this lookahead procedure can be implemented efficiently by centered rank-one modifications.

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AAAI Conference 2021 Conference Paper

Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-Optimality

  • Guihong Wan
  • Haim Schweitzer

Outliers negatively affect the accuracy of data analysis. In this paper we are concerned with their influence on the accuracy of Principal Component Analysis (PCA). Algorithms that attempt to detect outliers and remove them from the data prior to applying PCA are sometimes called Robust PCA, or Robust Subspace Recovery algorithms. We propose a new algorithm for outlier detection that combines two ideas. The first is “chunk recursive elimination” that was used effectively to accelerate feature selection, and the second is combinatorial search, in a setting similar to A*. Our main result is showing how to combine these two ideas. One variant of our algorithm is guaranteed to compute an optimal solution according to some natural criteria, but its running time makes it impractical for large datasets. Other variants are much faster and come with provable bounds on sub-optimality. Experimental results show the effectiveness of the proposed approach.

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IJCAI Conference 2021 Conference Paper

Heuristic Search for Approximating One Matrix in Terms of Another Matrix

  • Guihong Wan
  • Haim Schweitzer

We study the approximation of a target matrix in terms of several selected columns of another matrix, sometimes called "a dictionary". This approximation problem arises in various domains, such as signal processing, computer vision, and machine learning. An optimal column selection algorithm for the special case where the target matrix has only one column is known since the 1970's, but most previously proposed column selection algorithms for the general case are greedy. We propose the first nontrivial optimal algorithm for the general case, using a heuristic search setting similar to the classical A* algorithm. We also propose practical sub-optimal algorithms in a setting similar to the classical Weighted A* algorithm. Experimental results show that our sub-optimal algorithms compare favorably with the current state-of-the-art greedy algorithms. They also provide bounds on how close their solutions are to the optimal solution.

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AAAI Conference 2019 Conference Paper

Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

  • Baokun He
  • Swair Shah
  • Crystal Maung
  • Gordon Arnold
  • Guihong Wan
  • Haim Schweitzer

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.

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v2026.09.13