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Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

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書(shū)目名稱(chēng)Machine Learning and Knowledge Discovery in Databases: Research Track
副標(biāo)題European Conference,
編輯Danai Koutra,Claudia Plant,Francesco Bonchi
視頻videohttp://file.papertrans.cn/621/620554/620554.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con
描述The multi-volume set LNAI 14169 until? 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023..The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.?.The volumes are organized in topical sections as follows:.Part I:.?Active Learning;?Adversarial Machine Learning;?Anomaly Detection; Applications; Bayesian Methods; Causality;? ?Clustering..Part II:?.?Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning..Part III:?.?Graph Neural Networks;?Graphs; Interpretability;?Knowledge Graphs; Large-scale Learning..Part IV:.??Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning;?Representation Learning..Part V:.??Robustness; Time Series; Transfer and Multitask Learning..Part VI:.??Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Intera
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer security; computer systems; compu
版次1
doihttps://doi.org/10.1007/978-3-031-43424-2
isbn_softcover978-3-031-43423-5
isbn_ebook978-3-031-43424-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Exploring the?Training Robustness of?Distributional Reinforcement Learning Against Noisy State Obserimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning?(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the con
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Label Shift Quantification with?Robustness Guarantees via?Distribution Feature Matchingframework, distribution feature matching (DFM), that recovers as particular instances various estimators introduced in previous literature. We derive a general performance bound for DFM procedures, improving in several key aspects upon previous bounds derived in particular cases. We then extend this
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DualMatch: Robust Semi-supervised Learning with?Dual-Level Interactiong methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning not robust. In this paper, we propose a novel SSL method called DualMatch, in which the clas
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Deep Imbalanced Time-Series Forecasting via?Local Discrepancy Densitypite their scarce occurrences in the training set (., data imbalance), abrupt changes incur loss that significantly contributes to the total loss (., heteroscedasticity). Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal state
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