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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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Efficient and effective budget-feasible mechanisms for submodular valuations

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Artificial Intelligence 2025年 345卷

作者： Han, Kai Zhang, Haotian Cui, Shuang Ministry of Education's Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai China School of Computing and Artificial Intelligence Shanghai University of Finance and Economics Shanghai China School of Computer Science and Technology Soochow University Jiangsu Province Suzhou City China

We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for submodular valuation functions, which has been extensively studied since the seminal paper of Singer [FOCS'10] due to their wide applications in crowdsourcing and social marketing. We propose TripleEagle, a novel algorithmic framework for designing BFMs, based on which we present several simple yet effective BFMs that achieve better approximation ratios than the state-of-the-art work. Moreover, our BFMs are the first in the literature to achieve linear query complexity under the value oracle model while ensuring obvious strategyproofness, making them more practical than the previous BFMs. We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results demonstrate the superiorities of our approach in terms of efficiency and effectiveness compared to the state-of-the-art BFMs. © 2025 Elsevier B.V.

关键词： Design

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Price Stability and Improved Buyer Utility with Presentation Design: A Theoretical Study of the Amazon Buy Box

arXiv

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arXiv 2025年

作者： Friedler, Ophir Fu, Hu Karlin, Anna Tang, Ariana Outbrain Netanya Israel Shanghai University of Finance and Economics Key Laboratory of Interdisciplinary Research of Computation and Economics Ministry of Education Shanghai China Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Shanghai University of Finance and Economics Shanghai China

Platforms design the form of presentation by which sellers are shown to the buyers. This design not only shapes the buyers’ experience but also leads to different market equilibria or dynamics. One component in this design is through the platform’s mediation of the search frictions experienced by the buyers for different sellers. We take a model of monopolistic competition and show that, on one hand, when all sellers have the same inspection costs, the market sees no stable price since the sellers always have incentives to undercut each other, and, on the other hand, the platform may stabilize the price by giving prominence to one seller chosen by a carefully designed mechanism. This calls to mind Amazon’s Buy Box. We study natural mechanisms for choosing the prominent seller, characterize the range of equilibrium prices implementable by them, and find that in certain scenarios the buyers’ surplus improves as the search friction increases. © 2025, CC BY.

关键词： Product design

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Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models 38

Cherry on Top: Parameter Heterogeneity and Quantization in L...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cui, Wanyun Wang, Qianle Shanghai University of Finance and Economics China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China

This paper reveals the phenomenon of parameter heterogeneity in large language models (LLMs). We find that a small subset of "cherry" parameters exhibit a disproportionately large influence on model performance, while the vast majority of parameters have minimal impact. This heterogeneity is found to be prevalent across different model families, scales, and types. Motivated by this observation, we propose CherryQ, a novel quantization method that unifies the optimization of mixed-precision parameters. CherryQ identifies and preserves the critical cherry parameters in high precision while aggressively quantizing the remaining parameters to low precision. Extensive experiments demonstrate the effectiveness of CherryQ. CherryQ outperforms existing quantization approaches in terms of perplexity and downstream task performance. Notably, our 3-bit quantized Vicuna-1.5 exhibits competitive performance compared to their 16-bit counterparts. © 2024 Neural information processing systems foundation. All rights reserved.

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Setting Targets is All You Need: Improved Order Competitive Ratio for Online Selection 24

Setting Targets is All You Need: Improved Order Competitive ...

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25th Conference on economics and computation, EC 2024

作者： Chen, Liyan Sun, Nuozhou Tang, Zhihao Gavin Institute for Interdisciplinary Information Sciences Tsinghua University China ITCS Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China

ISBN: (纸本)9798400707049

There is a rising interest for studying the online benchmark as an alternative of the classical offline benchmark in online stochastic settings. Ezra, Feldman, Gravin, and Tang (SODA 2023) introduced the notion of order-competitive ratio, defined as the worst-case ratio between the performance of the best order-unaware algorithm and the best order-aware algorithm, to quantify the loss incurred by the lack of knowledge of the arrival order. They showed in the online single selection setting (a.k.a. the prophet problem), the optimal order-competitive ratio achieved by deterministic algorithms is 1/φ ≈ 0.618, and left with an open question whether randomized algorithms can do better. We answer the open question firmly by introducing a novel family of algorithms called targeted value algorithms. We show that the task of online selection is as easy as guessing the optimal online benchmark. Specifically, we provide 1) an alternative optimal 1/φ order-competitive algorithm by setting the targeted value deterministically, and 2) a 0.732 order-competitive algorithm by setting the targeted value randomly. We further provide a 0.758 upper bound on the order-competitive ratio of our algorithm, showing that our analysis is close to the best possible, and establish an upper bound of 0.829 on the order-competitive ratio for general randomized order-unaware algorithms. © 2024 Copyright held by the owner/author(s).

关键词： Consensus algorithm

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Prediction Accuracy of Learning in Games: Follow-the-Regularized-Leader meets Heisenberg 41

Prediction Accuracy of Learning in Games: Follow-the-Regular...

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41st International Conference on Machine Learning, ICML 2024

作者： Feng, Yi Piliouras, Georgios Wang, Xiao Shanghai University of Finance and Economics Shanghai China Google DeepMind London United Kingdom Key Laboratory of Interdisciplinary Research of Computation and Economics China

We investigate the accuracy of prediction in deterministic learning dynamics of zero-sum games with random initializations, specifically focusing on observer uncertainty and its relationship to the evolution of covariances. Zero-sum games are a prominent field of interest in machine learning due to their various applications. Concurrently, the accuracy of prediction in dynamical systems from mechanics has long been a classic subject of investigation since the discovery of the Heisenberg Uncertainty Principle. This principle employs covariance and standard deviation of particle states to measure prediction accuracy. In this study, we bring these two approaches together to analyze the Follow-the-Regularized-Leader (FTRL) algorithm in two-player zero-sum games. We provide growth rates of covariance information for continuous-time FTRL, as well as its two canonical discretization methods (Euler and Symplectic). A Heisenberg-type inequality is established for FTRL. Our analysis and experiments also show that employing Symplectic discretization enhances the accuracy of prediction in learning dynamics. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Incentives for Early Arrival in Cooperative Games 23

Incentives for Early Arrival in Cooperative Games

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Ge, Yaoxin Zhang, Yao Zhao, Dengji Tang, Zhihao Gavin Fu, Hu Lu, Pinyan Key Laboratory of Intelligent Perception and Human-Machine Collaboration ShanghaiTech University Shanghai China Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

We study cooperative games where players join sequentially, and the value generated by those who have joined at any point must be irrevocably divided among these players. We introduce two desiderata for the value division mechanism: that the players should have incentives to join as early as possible, and that the division should be considered fair. For the latter, we require that each player's expected share in the mechanism should equal her Shapley value if the players' arrival order is uniformly at random. When the value generation function is submodular, allocating the marginal value to the player satisfies these properties. This is no longer true for more general functions. Our main technical contribution is a complete characterization of 0-1 value games for which desired mechanisms exist. We show that a natural mechanism, Rewarding First Critical Player (RFC), is complete, in that a 0-1 value function admits a mechanism with the properties above if and only if RFC satisfies them;we analytically characterize all such value functions. Moreover, we give an algorithm that decomposes, in an online fashion, any value function into 0-1 value functions, on each of which RFC can be run. In this way, we design an extension of RFC for general monotone games, and the properties are proved to be maintained. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Multi agent systems

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Distract Large Language Models for Automatic Jailbreak Attack

Distract Large Language Models for Automatic Jailbreak Attac...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xiao, Zeguan Yang, Yan Chen, Guanhua Chen, Yun Shanghai University of Finance and Economics China Southern University of Science and Technology China Key Laboratory of Interdisciplinary Research of Computation and Economics Ministry of Education China

ISBN: (纸本)9798891761643

Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading to unintended behaviors. In this work, we propose a novel black-box jailbreak framework for automated red teaming of LLMs. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs, motivated by the research about the distractibility and over-confidence phenomenon of LLMs. Extensive experiments of jailbreaking both open-source and proprietary LLMs demonstrate the superiority of our framework in terms of effectiveness, scalability and transferability. We also evaluate the effectiveness of existing jailbreak defense methods against our attack and highlight the crucial need to develop more effective and practical defense strategies. © 2024 Association for computational Linguistics.

关键词： Open source software

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SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation 38

SeTAR: Out-of-Distribution Detection with Selective Low-Rank...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Yixia Xiong, Boya Chen, Guanhua Chen, Yun Southern University of Science and Technology China Shanghai University of Finance and Economics China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China

Out-of-distribution (OOD) detection is crucial for the safe deployment of neural networks. Existing CLIP-based approaches perform OOD detection by devising novel scoring functions or sophisticated fine-tuning methods. In this work, we propose SeTAR, a novel, training-free OOD detection method that leverages selective low-rank approximation of weight matrices in vision-language and vision-only models. SeTAR enhances OOD detection via post-hoc modification of the model's weight matrices using a simple greedy search algorithm. Based on SeTAR, we further propose SeTAR+FT, a fine-tuning extension optimizing model performance for OOD detection tasks. Extensive evaluations on ImageNet1K and Pascal-VOC benchmarks show SeTAR's superior performance, reducing the relatively false positive rate by up to 18.95% and 36.80% compared to zero-shot and fine-tuning baselines. Ablation studies further validate SeTAR's effectiveness, robustness, and generalizability across different model backbones. Our work offers a scalable, efficient solution for OOD detection, setting a new state-of-the-art in this area. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved Bounds for Fractional Online Matching Problems 24

Improved Bounds for Fractional Online Matching Problems

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25th Conference on economics and computation, EC 2024

作者： Tang, Zhihao Gavin Zhang, Yuhao ITCS Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400707049

Online bipartite matching with one-sided arrival and its variants have been extensively studied since the seminal work of Karp, Vazirani, and Vazirani (STOC 1990). Motivated by real-life applications with dynamic market structures, e.g., ride-sharing, two generalizations of the classical one-sided arrival model are proposed to allow non-bipartite graphs and to allow all vertices to arrive online. Namely, online matching with general vertex arrival is introduced by Wang and Wong (ICALP 2015), and fully online matching is introduced by Huang et al. (JACM 2020). In this paper, we study the fractional versions of the two models. We improve three out of the four state-of-the-art upper and lower bounds of the two models. For fully online matching, we design a 0.6-competitive algorithm and prove that no algorithm can be 0.613-competitive. For online matching with general vertex arrival, we prove that no algorithm can be 0.584-competitive. Moreover, we give an arguably more intuitive algorithm for the general vertex arrival model, compared to the algorithm of Wang and Wong, while attaining the same competitive ratio of 0.526 (in closed form). © 2024 Copyright held by the owner/author(s).

关键词： Consensus algorithm

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