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检索条件"任意字段=18th International Conference on Algorithmic Learning Theory"

共 549 条记录，以下是1-10 订阅

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algorithmic learning theory 18th international conference, ALT 2007 Proceedings

Algorithmic Learning Theory 18th International Conference, A...

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18th international conference on algorithmic learning theory, ALT 2007

ISBN: (纸本)9783540752240

the proceedings contain 29 papers. the topics discussed include: a theory of similarity functions for learning and clustering;machine learning in ecosystem informatics;a Hilbert space embedding for distributions;simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity and creativity;feasible iteration of feasible learning functionals;parallelism increases iterative learning power;learning in friedberg numberings;separating models of learning with faulty teachers;parameterized learnability of k-juntas and related problems;tuning bandit algorithms in stochastic environments;online regression competitive with changing predictors;cluster identification in nearest-neighbor graphs;learning efficiency of very simple grammars from positive data;learning national stochastic tree languages;and polynomial time algorithms for learning k-reversible languages and pattern languages with correction queries.

关键词： learning systems

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18th international Joint conference on theoretical Computer Science-Frontier of algorithmic Wisdom, IJTCS-FAW 2024

18th International Joint Conference on Theoretical Computer ...

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18th international Joint conference on theoretical Computer Science-Frontier of algorithmic Wisdom, IJTCS-FAW 2024

ISBN: (纸本)9789819777518

the proceedings contain 23 papers. the special focus in this conference is on theoretical Computer Science-Frontier of algorithmic Wisdom. the topics include: On the Existence of EFX (and Pareto-Optimal) Allocations for Binary Chores;how to Play Old Maid with Virtual Players;algorithms for Optimally Shifting Intervals Under Intersection Graph Models;on the Fine-Grained Complexity of Approximating Max k-Coverage;nested and Interleaved Ticketing for Multiple Travelers;longest (k]-Tuple Common Substrings;scheduling Two Types of Jobs with Minimum Makespan;blockchain Technology for Digital Asset Ownership;on the Optimal Mixing Problem of Approximate Nash Equilibria in Bimatrix Games;finding Fair and Efficient Allocations Under Budget Constraints;computations and Complexities of Tarski’s Fixed Points and Supermodular Games;parity-Constrained k-Supplier Problem;approximating Principal-Agent Problem Under Bayesian;robust Facility Leasing Problem with Penalties;randomized Strategyproof Mechanisms for Multi-Stage Facility Location Problem with Capacity Constraints;from Evolutionary Game Dynamics to Non-negative Matrix Factorization: Acceleration with Hessian Geometry;a Case for Copeland: from theory to Practice;deterministic and Universal Truthful Mechanism for Fair Matching;equilibrium Strategies of Carbon Emission Reduction in Agricultural Product Supply Chain Under Carbon Sink Trading;active learning Supported Iterative Combinatorial Auctions;locating Two Facilities on a Square with a Minimum Distance Requirement.

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8th international conference on algorithmic Decision theory, ADT 2024

8th International Conference on Algorithmic Decision Theory,...

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8th international conference on algorithmic Decision theory, ADT 2024

ISBN: (纸本)9783031739026

the proceedings contain 18 papers. the special focus in this conference is on algorithmic Decision theory. the topics include: On Approximately Strategy-Proof Tournament Rules for Collusions of Size at Least three;as Time Goes By: Adding a Temporal Dimension to Resolve Delegations in Liquid Democracy;simple Stochastic Stopping Games: A Generator and Benchmark Library;protective and Nonprotective Subset Sum Games: A Parameterized Complexity Analysis;algorithmic Decision Analysis for Multi-stage Games with Incomplete Information;non-maximizing Policies that Fulfill Multi-criterion Aspirations in Expectation;adversarial Risk Analysis for Automated Lane-Changing in Heterogeneous Traffic;a Fully Bayesian Approach to Bilevel Problems;collaborative Information Dissemination with Graph-Based Multi-Agent Reinforcement learning;toward Fair and Strategyproof Tournament Rules for Tournaments with Partially Transferable Utilities;noise-Tolerant Active Preference learning for Multicriteria Choice Problems;learning Multiple Multicriteria Additive Models from Heterogeneous Preferences;Independent Relaxed Subproblems for Dominance Testing in CP-Nets;core Stability and Nash Stability in k-Tiered Coalition Formation Games;envy-Free and Efficient Allocations for Graphical Valuations;manipulation With(out) Money in Matching Market.

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Mind Over Machine: Evaluating theory of Mind Reasoning in LLMs and Humans 25

Mind Over Machine: Evaluating Theory of Mind Reasoning in LL...

引用

18th ACM international conference on Web Search and Data Mining, WSDM 2025

作者： Amirizaniani, Maryam University of Washington SeattleWA United States

ISBN: (纸本)9798400713293

As Large Language Models (LLMs) become increasingly integrated into applications demanding human-like understanding-such as mental health support, education, and social robotics-their capacity to exhibit theory of Mind (ToM) reasoning is essential. Although previous research has evaluated LLMs' capability in ToM tasks, a critical gap remains as few studies have systematically investigated how LLMs' ToM reasoning diverges from human reasoning and the extent of these differences. this study introduces a reinforcement learning-based framework designed to bridge this gap. this approach seeks to enhance LLM alignment with human ToM reasoning, effectively narrowing the differences in their reasoning processes. Finally, future research directions to advance this field will be discussed, including strategies for developing LLMs that can better approximate human social cognition. this work lays a foundation for responsible LLM deployment, offering guidelines for applications in sensitive contexts where accurate ToM understanding is crucial. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep learning Frameworks 18th

Enhancing Adversarial Robustness in Automatic Modulation Rec...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Li, Xiaohu Zhou, Yajian Yan, Hongchao Beijing University of Posts and Telecommunications Beijing100876 China The 54th Research Institute of China Electronics Technology Group Corporation HeBei050050 China

ISBN: (纸本)9783031714634

this study introduces a novel deep learning framework aimed at enhancing the defensive capabilities of Automatic Modulation Recognition (AMR) systems against adversarial attacks through the application of dynamical systems theory and an adaptive weight learning mechanism. Utilizing dynamical systems theory, we analyze and simulate the propagation process of adversarial perturbations, revealing the mechanisms behind various types of attacks, thereby identifying and reinforcing the model's intrinsic resistance to disturbances. At the same time, we introduce an adaptive weight learning mechanism capable of assessing and adjusting the weights of different features, optimizing the feature fusion process to improve the model's accuracy and robustness when facing unknown adversarial samples. through testing on standard AMR datasets, we have validated that our proposed framework significantly enhances the model's performance under both normal and adversarial conditions. the contributions of this paper lie not only in improving the adversarial defense capabilities of AMR systems but also in providing a new methodology for designing deep learning models using dynamical systems theory and adaptive learning mechanism. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Active learning Supported Iterative Combinatorial Auctions 18th

Active Learning Supported Iterative Combinatorial Auctions

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18th international Joint conference on theoretical Computer Science-Frontier of algorithmic Wisdom, IJTCS-FAW 2024

作者： Estermann, Benjamin Kramer, Stefan Wattenhofer, Roger Wang, Kanye Ye ETH Zurich Zurich Switzerland University of Macau China

ISBN: (纸本)9789819777518

In deep learning-based iterative combinatorial auctions (DL-ICA), bidders do not have to report valuations for all bundles up front. Instead, DL-ICA iteratively asks bidders to report their value for specific bundles and determines the allocation of the items using a winner determination problem. In the process, bidder profiles are modeled using neural networks. However, due to the relatively small number of reported bundles, DL-ICA may not always realize the optimal winner allocation, resulting in reduced economic efficiency. In this work, we improve the economic efficiency, i.e., the social welfare, of DL-ICA by optimizing the underlying machine learning-based elicitation algorithm. To this end, we introduce two different active learning-based initial sampling strategies, called GALI and GALO. GALI ensures an optimal coverage of the entire bundle space during sampling, while GALO tries to find bundles with a high diversity of the bidders’ value estimated by the underlying neural network. By doing so, our work extends the scope of active learning, which was previously constrained to small pool sizes. We show how linear programs can be used for active learning to handle pool sizes larger than 1030 samples. We prove the correctness of our approach theoretically and verify its performance experimentally. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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algorithmic Bias from the Perspectives of Healthcare Professionals

Algorithmic Bias from the Perspectives of Healthcare Profess...

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18th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Xu, Jennifer Babaian, Tamara Department of Computer Information Systems Bentley University WalthamMA United States

this paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adoption of ML algorithms and algorithm generated outcomes. the results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the algorithm characteristics. More research is needed to investigate algorithmic bias to ensure fairness and equality in healthcare. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive learning

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Fair and Communication-Efficient Personalized Federated learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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CONTINUOUS learning OF BIG DATA ANALYTICS OF EMPLOYEES: INTEGRATING UTAUT AND SITUATED EXPECTANCY-VALUE BELIEFS 18

CONTINUOUS LEARNING OF BIG DATA ANALYTICS OF EMPLOYEES: INTE...

引用

18th international conferences IADIS Information Systems 2025 and 23rd international conference on e-Society 2025

作者： Lee, Seula Lee, Hyewon Cho, Youngsang Management of Technology Yonsei University Seoul03722 Korea Republic of Gyeongi-do 16824 Korea Republic of Department of Industrial Engineering College of Engineering Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9789898704658

Despite recognizing the importance of big data analytics capabilities for competitive advantage, the understanding of employees’ skill development remains limited. While situated expectancy-value theory (SEVT) is useful for identifying factors influencing learning intention, it lacks actionable strategies for organizations to foster learning environments. Integrating the unified theory of acceptance and use of technology (UTAUT) with SEVT, this ongoing study addresses the gap in understanding how employees enhance their skills to use big data analytics effectively among semiconductor manufacturing engineers. Data were collected from 144 engineers at a semiconductor manufacturing company in South Korea. Partial least squares structural equation modeling was used to analyze the relationships between the constructs. the results show that performance expectancy, effort expectancy, and social influence positively affect continued intention to use big data analytics applications, except facilitating conditions. Secondly, continued intention to use and social norms as supportive environments of learning positively influence intrinsic motivation, mediated by factors such as self-efficacy and perceived usefulness. Understanding these factors provides actionable insights into how technological investment creates learning environments that facilitate the continuous development of big data analytics capabilities. By focusing on mechanisms that foster continuous learning, this study offers strategic guidance for organizations that aim to drive data-driven innovation and maintain a competitive advantage in high-tech industries. © 2025 Proceedings of the international conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Competition

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algorithmic learning theory: 14th international conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings 1

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丛书名： Lecture notes in computer science,

2003年

作者： thomas Eiter (auth.) Ricard Gavaldá Klaus P. Jantke Eiji Takimoto (eds.)

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