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Xiaolu Wang

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7 papers

2 author rows

JBHI Journal 2026 Journal Article

Interictal Epileptiform Discharge Detection Using Dual-Domain Features and GAN

Wenhao Rao
Jiayang Guo
Chunran Zhu
Meiyan Xu
Naian Xiao
Yijie Pan
Ling Zhang
Xiaowen Ye

Interictal Epileptiform Discharge is essential for identifying epilepsy. However, the unpredictable and non-stationary nature of electroencephalogram (EEG) patterns poses considerable challenges for reliable identification. Manual interpretation of EEG is subjective and time-consuming. With advancements in machine learning and deep learning, computer-aided approaches for automated IED detection have been rapidly developed. The state-of-the-art convolutional neural network (CNN)-based methods have shown promising results but struggle to capture long-term dependencies in time-series data. In contrast, Transformer excels at modeling sequential information through self-attention mechanisms, overcoming the CNN limitations. This study proposes an IED Detector (IEDD) that integrates convolutional layers and a Transformer to detect IEDs. The IEDD initially employs convolutional layers to extract local features of IEDs, followed by a Transformer to model long-term dependencies. To further extract spatial features, EEG data are represented as a three-dimensional tensor with embedded channel topology, where a CNN captures spatial features at each sampling point and a Long Short-Term Memory (LSTM) network models their temporal evolution. Additionally, due to the scarcity of IED data, a novel Transformer-based Generative Adversarial Network (GAN) is developed to augment the IED dataset. Experimental results show the proposed approach achieves an average accuracy of 96. 11% on the augmented Dataset 1 and 95. 25% on Dataset 2 for binary classification, with an average sensitivity of 87. 26% and precision of 89. 96% for multi-label classification. These findings provide valuable insights into advancing deep learning and Transformer-based approaches for automated IED detection.

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ICLR Conference 2025 Conference Paper

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning

Xinran Li
Xiaolu Wang
Chenjia Bai
Jun Zhang 0004

In cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performance. Moreover, in large-scale multi-agent systems commonly found in real-world applications, effective communication plays an even more critical role due to the escalated challenge of partial observability compared to smaller-scale setups. In this work, we endeavor to develop a scalable communication protocol for MARL. Unlike previous methods that focus on selecting optimal pairwise communication links—a task that becomes increasingly complex as the number of agents grows—we adopt a global perspective on communication topology design. Specifically, we propose utilizing the exponential topology to enable rapid information dissemination among agents by leveraging its small-diameter and small-size properties. This approach leads to a scalable communication protocol, named ExpoComm. To fully unlock the potential of exponential graphs as communication topologies, we employ memory-based message processors and auxiliary tasks to ground messages, ensuring that they reflect global information and benefit decision-making. Extensive experiments on large-scale cooperative benchmarks, including MAgent and Infrastructure Management Planning, demonstrate the superior performance and robust zero-shot transferability of ExpoComm compared to existing communication strategies. The code is publicly available at [https://github.com/LXXXXR/ExpoComm](https://github.com/LXXXXR/ExpoComm).

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ICML Conference 2025 Conference Paper

Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning

Wenjing Yan
Xiangyu Zhong
Xiaolu Wang
Ying-Jun Angela Zhang

Asynchronous federated learning (AFL) has emerged as a promising solution to address system heterogeneity and improve the training efficiency of federated learning. However, existing AFL methods face two critical limitations: 1) they rely on strong assumptions about bounded data heterogeneity across clients, and 2) they require meticulous tuning of learning rates based on unknown system parameters. In this paper, we tackle these challenges by leveraging momentum-based optimization and adaptive learning strategies. We first propose MasFL, a novel momentum-driven AFL framework that successfully eliminates the need for data heterogeneity bounds by effectively utilizing historical descent directions across clients and iterations. By mitigating the staleness accumulation caused by asynchronous updates, we prove that MasFL achieves state-of- the-art convergence rates with linear speedup in both the number of participating clients and local updates. Building on this foundation, we further introduce AdaMasFL, an adaptive variant that incorporates gradient normalization into local updates. Remarkably, this integration removes all dependencies on problem-specific parameters, yielding a fully tuning-free AFL approach while retaining theoretical guarantees. Extensive experiments demonstrate that AdaMasFL consistently outperforms state-of-the-art AFL methods in run- time efficiency and exhibits exceptional robustness across diverse learning rate configurations and system conditions.

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ICLR Conference 2025 Conference Paper

Problem-Parameter-Free Federated Learning

Wenjing Yan
Kai Zhang
Xiaolu Wang
Xuanyu Cao

Federated learning (FL) has garnered significant attention from academia and industry in recent years due to its advantages in data privacy, scalability, and communication efficiency. However, current FL algorithms face a critical limitation: their performance heavily depends on meticulously tuned hyperparameters, particularly the learning rate or stepsize. This manual tuning process is challenging in federated settings due to data heterogeneity and limited accessibility of local datasets. Consequently, the reliance on problem-specific parameters hinders the widespread adoption of FL and potentially compromises its performance in dynamic or diverse environments. To address this issue, we introduce PAdaMFed, a novel algorithm for nonconvex FL that carefully combines adaptive stepsize and momentum techniques. PAdaMFed offers two key advantages: 1) it operates autonomously without relying on problem-specific parameters; and 2) it manages data heterogeneity and partial participation without requiring heterogeneity bounds. Despite these benefits, PAdaMFed provides several strong theoretical guarantees: 1) It achieves state-of-the-art convergence rates with a sample complexity of $\mathcal{O}(\epsilon^{-4})$ and communication complexity of $\mathcal{O}(\epsilon^{-3})$ to obtain an accuracy of $||\nabla f\left(\boldsymbol{\theta}\right)|| \leq \epsilon$, even using constant learning rates; 2) these complexities can be improved to the best-known $\mathcal{O}(\epsilon^{-3})$ for sampling and $\mathcal{O}(\epsilon^{-2})$ for communication when incorporating variance reduction; 3) it exhibits linear speedup with respect to the number of local update steps and participating clients at each global round. These attributes make PAdaMFed highly scalable and adaptable for various real-world FL applications. Extensive empirical evidence on both image classification and sentiment analysis tasks validates the efficacy of our approaches.

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ICML Conference 2023 Conference Paper

Network Effects in Performative Prediction Games

Xiaolu Wang
Chung-Yiu Yau
Hoi-To Wai

This paper studies the multi-agent performative prediction (Multi-PP) games over multiplex networks. We consider a distributed learning setting where agents partially cooperate on an agent network, while during learning, the data samples drawn depend on the prediction models of the agent itself and neighboring agents on a population network. The dynamics of Multi-PP games is hence affected by the interplay between both networks. This paper concentrates on this Multi-PP game with the following contributions. Firstly, we analyze sufficient conditions for the existence of the performative stable equilibrium (PSE) and Nash equilibrium (NE) of the Multi-PP games. Secondly, we analyze the changes to the equilibrium induced by perturbed data distributions, and derive the closed-form solutions where the network topologies are explicit. Our results connect the existence of PSE/NE with strengths of agents’ cooperation, and the changes of equilibrium solutions across agents with their node centrality, etc. Lastly, we show that a stochastic gradient descent (SGD) based distributed learning procedure finds the PSE under the said sufficient condition. Numerical illustrations on the network effects in Multi-PP games corroborate our findings.

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ICML Conference 2023 Conference Paper

Projected Tensor Power Method for Hypergraph Community Recovery

Jinxin Wang
Yuen-Man Pun
Xiaolu Wang
Peng Wang 0098
Anthony Man-Cho So

This paper investigates the problem of exact community recovery in the symmetric $d$-uniform $(d \geq 2)$ hypergraph stochastic block model ($d$-HSBM). In this model, a $d$-uniform hypergraph with $n$ nodes is generated by first partitioning the $n$ nodes into $K\geq 2$ equal-sized disjoint communities and then generating hyperedges with a probability that depends on the community memberships of $d$ nodes. Despite the non-convex and discrete nature of the maximum likelihood estimation problem, we develop a simple yet efficient iterative method, called the projected tensor power method, to tackle it. As long as the initialization satisfies a partial recovery condition in the logarithmic degree regime of the problem, we show that our proposed method can exactly recover the hidden community structure down to the information-theoretic limit with high probability. Moreover, our proposed method exhibits a competitive time complexity of $\mathcal{O}(n\log^2n/\log\log n)$ when the aforementioned initialization condition is met. We also conduct numerical experiments to validate our theoretical findings.

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ECAI Conference 2020 Conference Paper

SETNet: A Novel Semi-Supervised Approach for Semantic Parsing

Xiaolu Wang
Haifeng Sun 0001
Qi Qi 0001
Jingyu Wang 0001

In this work, we study on semi-supervised semantic parsing under a multi-task learning framework to alleviate limited performance caused by limited annotated data. Two novel strategies are proposed to leverage unlabeled natural language utterances. The first one takes entity predicate sequences as training targets to enhance representation learning. The second one extends Mean Teacher to seq2seq model and generates more target-side data to improve the generalizability of decoder network. Different from original Mean Teacher, our strategy produces hard targets for the student decoder and update the decoder weights instead of the whole model. Experiments demonstrate that our proposed methods significantly outperform the supervised baseline and achieve more impressive improvement than previous methods.

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