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Author ECML PKDD (Conference) (2021 : Online)

Title Machine learning and knowledge discovery in databases : Research track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. Part I / Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano (eds.)
Published Cham, Switzerland : Springer, 2021

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Description 1 online resource (xli, 806 pages) : illustrations (some color)
Series Lecture notes in artificial intelligence
Lecture notes in computer science ; 12975
LNCS sublibrary, SL 7, Artificial intelligence
Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12975.
LNCS sublibrary. SL 7, Artificial intelligence.
Contents Intro -- Preface -- Organization -- Invited Talks Abstracts -- WuDao: Pretrain the World -- The Value of Data for Personalization -- AI Fairness in Practice -- Safety and Robustness for Deep Learning with Provable Guarantees -- Contents -- Part I -- Online Learning -- Routine Bandits: Minimizing Regret on Recurring Problems -- 1 Introduction -- 2 The Routine Bandit Setting -- 3 The KLUCB-RB Strategy -- 4 Sketch of Proof -- 5 Numerical Experiments -- 5.1 More Arms Than Bandits: A Beneficial Case -- 5.2 Increasing the Number of Bandit Instances -- 5.3 Critical Settings -- 6 Conclusion
3 Knowledge Infused Policy Gradients -- 4 Formulation of Knowledge Infusion -- 5 Regret Bound for KIPG -- 6 KIPG-Upper Confidence Bound -- 7 Experiments -- 7.1 Simulated Domains -- 7.2 Real-World Datasets -- 8 Conclusion and Future Work -- References -- Exploiting History Data for Nonstationary Multi-armed Bandit -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 The BR-MAB Algorithm -- 4.1 Break-Point Prediction Procedure -- 4.2 Recurrent Concepts Equivalence Test -- 4.3 Regret Analysis for Generic CD-MABs -- 4.4 Regret Analysis for the Break-Point Prediction Procedure
5 Experiments -- 5.1 Toy Example -- 5.2 Synthetic Setting -- 5.3 Yahoo! Setting -- 6 Conclusion and Future Works -- References -- High-Probability Kernel Alignment Regret Bounds for Online Kernel Selection -- 1 Introduction -- 1.1 Related Work -- 2 Problem Setting -- 3 A Nearly Optimal High-Probability Regret Bound -- 3.1 Warm-Up -- 3.2 A More Efficient Algorithm -- 3.3 Regret Bound -- 3.4 Time Complexity Analysis -- 4 Regret-Performance Trade-Off -- 4.1 Regret Bound -- 4.2 Budgeted EA2OKS -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusion -- References
Reinforcement Learning -- Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Method -- 4.1 Overview -- 4.2 Ensemble Initialization -- 4.3 Joint Training -- 4.4 Intra-ensemble Knowledge Distillation -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Effectiveness of PIEKD -- 5.3 Effectiveness of Knowledge Distillation for Knowledge Sharing -- 5.4 Effectiveness of Selecting the Best-Performing Agent as the Teacher -- 5.5 Ablation Study on Ensemble Size -- 5.6 Ablation Study on Distillation Interval -- 6 Conclusion
Summary The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media
Notes "Unfortunately it had to be held online and we could only meet each other virtually."--Preface
Includes author index
Online resource; title from PDF title page (SpringerLink, viewed September 16, 2021)
Subject Machine learning -- Congresses
Data mining -- Congresses
Data mining
Machine learning
Genre/Form proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Form Electronic book
Author Oliver, Nuria, 1970- editor
Pérez-Cruz, Fernando, editor.
Kramer, Stefan, Prof. Dr., editor.
Read, Jesse, editor
Lozano, José A., 1968- editor.
ISBN 9783030864866
3030864863
Other Titles ECML PKDD 2021
Research track