{"title":"Danai Koutra","description":null,"products":[{"product_id":"machine-learning-and-knowledge-discovery-in-databases-research-track-book-danai-koutra-9783031434204","title":"Machine Learning and Knowledge Discovery in Databases: Research Track","description":"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.\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe volumes are organized in topical sections as follows:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering.\u003c\/p\u003e\u003cb\u003ePart II: \u003c\/b\u003e​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart III: \u003c\/b\u003e​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e ​Natural Language Processing; Neuro\/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.\u003c\/p\u003e\u003cb\u003ePart V:\u003c\/b\u003e ​Robustness; Time Series; Transfer and Multitask Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart VI:\u003c\/b\u003e ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare \u0026amp; Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.\u003c\/p\u003e\u003cp\u003e​\u003cb\u003ePart VII: \u003c\/b\u003eSustainability, Climate, and Environment.- Transportation \u0026amp; Urban Planning.- Demo.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":50656303808785,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":50656303972625,"sku":"NGR9783031434204","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52138329571601,"sku":"NLS9783031434204","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/303143420X.jpg?v=1785796526"},{"product_id":"machine-learning-and-knowledge-discovery-in-databases-research-track-book-danai-koutra-9783031434112","title":"Machine Learning and Knowledge Discovery in Databases: Research Track","description":"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.\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe volumes are organized in topical sections as follows:\u003cbr\u003e\u003c\/p\u003e\n\n\u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering.\u003c\/p\u003e\u003cb\u003ePart II: \u003c\/b\u003e​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.\u003cbr\u003e \u003cp\u003e\u003cb\u003ePart III: \u003c\/b\u003e​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.\u003c\/p\u003e\n\n\u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e ​Natural Language Processing; Neuro\/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.\u003c\/p\u003e\u003cb\u003ePart V:\u003c\/b\u003e ​Robustness; Time Series; Transfer and Multitask Learning.\u003cbr\u003e \u003cp\u003e\u003cb\u003ePart VI:\u003c\/b\u003e ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare \u0026amp; Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.\u003c\/p\u003e\u003cp\u003e​\u003cb\u003ePart VII: \u003c\/b\u003eSustainability, Climate, and Environment.- Transportation \u0026amp; Urban Planning.- Demo.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52123859812625,"sku":"NLS9783031434112","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031434112.jpg?v=1757453615"},{"product_id":"machine-learning-and-knowledge-discovery-in-databases-research-track-book-danai-koutra-9783031434235","title":"Machine Learning and Knowledge Discovery in Databases: Research Track","description":"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.\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe volumes are organized in topical sections as follows:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering.\u003c\/p\u003e\u003cb\u003ePart II: \u003c\/b\u003e​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart III: \u003c\/b\u003e​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e ​Natural Language Processing; Neuro\/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.\u003c\/p\u003e\u003cb\u003ePart V:\u003c\/b\u003e ​Robustness; Time Series; Transfer and Multitask Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart VI:\u003c\/b\u003e ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare \u0026amp; Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.\u003c\/p\u003e\u003cp\u003e​\u003cb\u003ePart VII: \u003c\/b\u003eSustainability, Climate, and Environment.- Transportation \u0026amp; Urban Planning.- Demo.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52125518070033,"sku":"NLS9783031434235","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031434235.jpg?v=1785788065"},{"product_id":"machine-learning-and-knowledge-discovery-in-databases-research-track-book-danai-koutra-9783031434143","title":"Machine Learning and Knowledge Discovery in Databases: Research Track","description":"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.\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThe volumes are organized in topical sections as follows:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering.\u003c\/p\u003e\u003cb\u003ePart II: \u003c\/b\u003e​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart III: \u003c\/b\u003e​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e ​Natural Language Processing; Neuro\/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.\u003c\/p\u003e\u003cb\u003ePart V:\u003c\/b\u003e ​Robustness; Time Series; Transfer and Multitask Learning.\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart VI:\u003c\/b\u003e ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare \u0026amp; Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.\u003c\/p\u003e\u003cp\u003e​\u003cb\u003ePart VII: \u003c\/b\u003eSustainability, Climate, and Environment.- Transportation \u0026amp; Urban Planning.- Demo.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52125598712081,"sku":"NLS9783031434143","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031434143.jpg?v=1787946733"},{"product_id":"individual-and-collective-graph-mining-book-danai-koutra-9783031007835","title":"Individual and Collective Graph Mining","description":"Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company?\nThis book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas:\n\u003cul\u003e\n\u003cli\u003eIndividual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities.\u003c\/li\u003e\n\u003cli\u003eCollective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity.\u003c\/li\u003e\n\u003c\/ul\u003e\nThe methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science,to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52427321770257,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52427322786065,"sku":"NLS9783031007835","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031007835.jpg?v=1786791250"}],"url":"https:\/\/www.worldofbooks.com\/collections\/author-books-by-danai-koutra.oembed","provider":"World of Books ","version":"1.0","type":"link"}