Author name cluster

Xiaohu Wu

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Learning Structurally Stabilized Representations for Lossless DNA Storage

Ben Cao
Xue Li
Tiantian He
Bin Wang
Shihua Zhou
Xiaohu Wu
Qiang Zhang

This paper presents Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for lossless DNA data storage. In contrast to existing learning-based methods, RSRL is inspired by both error-correction codec and structural biology. Specifically, RSRL first learns the representations for the subsequent storage from the binary data transformed by the Reed-Solomon codec (RS code). Then, the representations are masked by an RS-code-informed mask to focus on correcting the burst errors occurring in the learning process. The synergy of RS masks and graph attention enables active error localization, breaking through the limitations of traditional passive error correction. With the decoded representations with error corrections, a novel biologically stabilized loss is formulated to regularize the data representations to possess stable single-stranded structures. By incorporating these novel strategies, RSRL can learn highly durable, dense, and lossless representations for subsequent storage tasks in DNA sequences. The proposed RSRL has been compared with a number of baselines in real-world tasks of multi-type data storage. The experimental results obtained demonstrate that RSRL can store diverse types of data with much higher information density and durability, but much lower error rates.

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ICML Conference 2025 Conference Paper

iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection

Huahui Yi
Wei Xu 0046
Ziyuan Qin 0001
Xi Chen
Xiaohu Wu
Kang Li 0004
Qicheng Lao

Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the tight coupling between foreground-background information and the coupled attention between prompts and image-text tokens present significant challenges in incremental medical object detection tasks, due to the conceptual gap between medical and natural domains. To overcome these challenges, we introduce the iDPA framework, which comprises two main components: 1) Instance-level Prompt Generation (IPG), which decouples fine-grained instance-level knowledge from images and generates prompts that focus on dense predictions, and 2) Decoupled Prompt Attention (DPA), which decouples the original prompt attention, enabling a more direct and efficient transfer of prompt information while reducing memory usage and mitigating catastrophic forgetting. We collect 13 clinical, cross-modal, multi-organ, and multi-category datasets, referred to as ODinM-13, and experiments demonstrate that iDPA outperforms existing SOTA methods, with FAP improvements of f 5. 44%, 4. 83%, 12. 88%, and 4. 59% in full data, 1-shot, 10-shot, and 50-shot settings, respectively.

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ICML Conference 2025 Conference Paper

Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning

Mengmeng Chen
Xiaohu Wu
Qiqi Liu
Tiantian He 0001
Yew-Soon Ong
Yaochu Jin
Qicheng Lao
Han Yu 0001

Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e. g. , it is fundamental for multiple avenues of research in federated learning (FL). Pareto-Front Learning (PFL) is a powerful method implemented using Hypernetworks (PHNs) to approximate the Pareto front. This method enables the acquisition of a mapping function from a given preference vector to the solutions on the Pareto front. However, most existing PFL approaches still face two challenges: (a) sampling rays in high-dimensional spaces; (b) failing to cover the entire Pareto Front which has a convex shape. Here, we introduce a novel PFL framework, called as PHN-HVVS, which decomposes the design space into Voronoi grids and deploys a genetic algorithm (GA) for Voronoi grid partitioning within high-dimensional space. We put forward a new loss function, which effectively contributes to more extensive coverage of the resultant Pareto front and maximizes the HV Indicator. Experimental results on multiple MOO machine learning tasks demonstrate that PHN-HVVS outperforms the baselines significantly in generating Pareto front. Also, we illustrate that PHN-HVVS advances the methodologies of several recent problems in the FL field. The code is available at https: //github. com/buptcmm/phnhvvs.

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AAAI Conference 2024 Conference Paper

FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants

Shanli Tan
Hao Cheng
Xiaohu Wu
Han Yu
Tiantian He
Yew Soon Ong
Chongjun Wang
Xiaofeng Tao

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely “the friend of my enemy is my enemy”, to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs.

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NeurIPS Conference 2024 Conference Paper

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning

Mengmeng Chen
Xiaohu Wu
Xiaoli Tang
Tiantian He
Yew-Soon Ong
Qiqi Liu
Qicheng Lao
Han Yu

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -FedEgoists- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities.

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AIJ Journal 2024 Journal Article

Polarized message-passing in graph neural networks

Tiantian He
Yang Liu
Yew-Soon Ong
Xiaohu Wu
Xin Luo

In this paper, we present Polarized message-passing (PMP), a novel paradigm to revolutionize the design of message-passing graph neural networks (GNNs). In contrast to existing methods, PMP captures the power of node-node similarity and dissimilarity to acquire dual sources of messages from neighbors. The messages are then coalesced to enable GNNs to learn expressive representations from sparse but strongly correlated neighbors. Three novel GNNs based on the PMP paradigm, namely PMP graph convolutional network (PMP-GCN), PMP graph attention network (PMP-GAT), and PMP graph PageRank network (PMP-GPN) are proposed to perform various downstream tasks. Theoretical analysis is also conducted to verify the high expressiveness of the proposed PMP-based GNNs. In addition, an empirical study of five learning tasks based on 12 real-world datasets is conducted to validate the performances of PMP-GCN, PMP-GAT, and PMP-GPN. The proposed PMP-GCN, PMP-GAT, and PMP-GPN outperform numerous strong message-passing GNNs across all five learning tasks, demonstrating the effectiveness of the proposed PMP paradigm.

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JBHI Journal 2023 Journal Article

Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry

Jun Gao
Qicheng Lao
Paul Liu
Huahui Yi
Qingbo Kang
Zekun Jiang
Xiaohu Wu
Kang Li

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.

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IJCAI Conference 2023 Conference Paper

Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation

Hao Cheng
Shufeng Kong
Yanchen Deng
Caihua Liu
Xiaohu Wu
Bo An
Chongjun Wang

Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to maximize the total utilities of all bidders. However, the MRC rule can suffer from severe unfairness since it ignores individuals' utilities. To address this limitation, we propose to explore the leximin principle to achieve fairness in core-selecting CAs since the leximin principle prefers to maximize the utility of the worst-off; the resulting bidder-leximin-optimal (BLO) payment rule is then theoretically analyzed and an effective algorithm is further provided to compute the BLO outcome. Moreover, we conduct extensive experiments to show that our algorithm returns fairer utility distributions and is faster than existing algorithms of core-selecting payment rules.

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SoCS Conference 2021 Conference Paper

Multi-Agent Pickup and Delivery with Task Deadlines

Xiaohu Wu
Yihao Liu 0002
Xueyan Tang
Wentong Cai 0001
Funing Bai
Gilbert Khonstantine
Guopeng Zhao

We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute tasks with individual deadlines to maximize the number of tasks completed by their deadlines. We take an integrated approach that assigns and plans one task at a time taking into account the agent states resulting from all the previous task assignments and path planning. We define metrics to effectively determine which agent ought to execute a given task and which task is most worth assignment next. We leverage the bounding technique to greatly improve the computational efficiency.

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