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ORCIDPKDD/ECML 2019: Würzburg, Germany - Workshops
- Peggy Cellier, Kurt Driessens
:
Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I. Communications in Computer and Information Science 1167, Springer 2020, ISBN 978-3-030-43822-7
Automating Data Science
- Laurens A. Castelijns, Yuri Maas, Joaquin Vanschoren:
The ABC of Data: A Classifying Framework for Data Readiness. 3-16 - Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo:
Automating Common Data Science Matrix Transformations. 17-27 - Claas Völcker, Alejandro Molina, Johannes Neumann, Dirk Westermann, Kristian Kersting:
DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets. 28-43 - Maryam Tavakol, Sebastian Mair, Katharina Morik:
HyperUCB: Hyperparameter Optimization Using Contextual Bandits. 44-50 - Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert:
Learning Parsers for Technical Drawings. 51-56 - Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales:
Meta-learning of Textual Representations. 57-67 - Xudong Sun, Jiali Lin, Bernd Bischl:
ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning. 68-84 - Emilia Oikarinen, Kai Puolamäki, Samaneh Khoshrou, Mykola Pechenizkiy:
Supervised Human-Guided Data Exploration. 85-101 - Yann Dauxais, Clément Gautrais, Anton Dries, Arcchit Jain, Samuel Kolb, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract). 102-110 - Franziska Horn, Robert Pack, Michael Rieger:
The autofeat Python Library for Automated Feature Engineering and Selection. 111-120 - Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer:
Towards Automated Configuration of Stream Clustering Algorithms. 137-143
Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining (AIMLAI-XKDD)
- Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid:
Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. 147-158 - Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini:
Global Explanations with Local Scoring. 159-171 - Christina Göpfert, Jan Philip Göpfert, Barbara Hammer:
Adversarial Robustness Curves. 172-179 - Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid:
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. 180-192 - Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. 193-204 - Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. 205-216 - Harish S. Bhat:
Learning and Interpreting Potentials for Classical Hamiltonian Systems. 217-228 - Maximilian Idahl, Megha Khosla, Avishek Anand:
Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases. 229-240 - Tiago Botari, Rafael Izbicki, André C. P. L. F. de Carvalho:
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. 241-252 - Juliana Cesaro, Fábio Gagliardi Cozman:
Measuring Unfairness Through Game-Theoretic Interpretability. 253-264 - Ioannis Mollas, Nikolaos Bassiliades, Grigorios Tsoumakas:
LioNets: Local Interpretation of Neural Networks Through Penultimate Layer Decoding. 265-276
Decentralized Machine Learning at the Edge
- Nico Piatkowski:
Distributed Generative Modelling with Sub-linear Communication Overhead. 281-292 - Mike Izbicki, Christian R. Shelton:
Distributed Learning of Neural Networks with One Round of Communication. 293-300 - John Klein, Mahmoud Albardan, Benjamin Guedj, Olivier Colot:
Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles. 301-316 - István Hegedüs, Gábor Danner, Márk Jelasity:
Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning. 317-332 - Mohsan Jameel, Josif Grabocka, Mofassir ul Islam Arif, Lars Schmidt-Thieme:
Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD. 333-341 - Sascha Mücke, Nico Piatkowski, Katharina Morik:
Hardware Acceleration of Machine Learning Beyond Linear Algebra. 342-347
Advances in Managing and Mining Large Evolving Graphs - 3rd Edition (LEG)
- Souâad Boudebza, Rémy Cazabet, Omar Nouali, Faiçal Azouaou:
Detecting Stable Communities in Link Streams at Multiple Temporal Scales. 353-367 - Lauranne Coppens, Jonathan De Venter, Sandra Mitrovic, Jochen De Weerdt:
A Comparative Study of Community Detection Techniques for Large Evolving Graphs. 368-384 - Sedigheh Mahdavi, Shima Khoshraftar, Aijun An:
Dynamic Joint Variational Graph Autoencoders. 385-401 - Christopher Rost, Andreas Thor, Philip Fritzsche, Kevin Gómez, Erhard Rahm:
Evolution Analysis of Large Graphs with Gradoop. 402-408 - Ying Yin, Jianpeng Zhang, Yulong Pei, Xiaotao Cheng, Lixin Ji:
MHDNE: Network Embedding Based on Multivariate Hawkes Process. 409-421
Data and Machine Learning Advances with Multiple Views
- Nicolas Audebert, Catherine Herold, Kuider Slimani, Cédric Vidal:
Multimodal Deep Networks for Text and Image-Based Document Classification. 427-443 - João P. B. Pereira, Erik S. G. Stroes, Albert K. Groen, Aeilko H. Zwinderman, Evgeni Levin:
Manifold Mixing for Stacked Regularization. 444-452 - Sebastian Pölsterl, Ignacio Sarasua, Benjamín Gutiérrez-Becker, Christian Wachinger:
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data. 453-464 - Mahdi Karami:
Deep Generative Multi-view Learning. 465-477
New Trends in Representation Learning with Knowledge Graphs (KGRL)
- Varun Ranganathan, Natarajan Subramanyam:
SDE-KG: A Stochastic Dynamic Environment for Knowledge Graphs. 483-488 - Weixin Zeng, Jiuyang Tang, Xiang Zhao:
Iterative Representation Learning for Entity Alignment Leveraging Textual Information. 489-494
Fourth Workshop on Data Science for Social Good (SoGood 2019)
- Tobias Bauer, Emre Devrim, Misha Glazunov, William Lopez Jaramillo, Balaganesh Mohan, Gerasimos Spanakis:
#MeTooMaastricht: Building a Chatbot to Assist Survivors of Sexual Harassment. 503-521 - José Mena, Marc Torrent-Moreno, Daniel González, Laura Portell, Oriol Pujol, Jordi Vitrià:
Analysis of Vocational Education and Training and the Labour Market in Catalonia. A Data-Driven Approach. 522-537 - Wen-Hao Chiang, Baichuan Yuan, Hao Li, Bao Wang, Andrea L. Bertozzi, Jeremy G. Carter, Brad Ray, George O. Mohler:
SOS-EW: System for Overdose Spike Early Warning Using Drug Mover's Distance-Based Hawkes Processes. 538-554 - Kai Wang, Bryan Wilder, Sze-Chuan Suen, Bistra Dilkina, Milind Tambe:
Improving GP-UCB Algorithm by Harnessing Decomposed Feedback. 555-569 - Guilherme Londres, Nuno Filipe, João Gama:
Optimizing Waste Collection: A Data Mining Approach. 570-578 - Sarah Cooney, Wendy Gomez, Kai Wang, Jorja Leap, P. Jeffrey Brantingham, Milind Tambe:
Mobile Game Theory with Street Gangs. 579-589 - Yair Horesh, Noa Haas, Elhanan Mishraky, Yehezkel S. Resheff, Shir Meir Lador:
Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning. 590-604 - Alicia Curth, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan P. de Bruin, Paul W. G. Elbers, Mattia Fornasa:
Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems. 605-621 - Afshin Sadeghi, Jens Lehmann:
Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter. 622-630 - Duncan Wallace, M. Tahar Kechadi:
Prediction of Frequent Out-Of-Hours' Medical Use. 631-646 - Lukas Pensel, Stefan Kramer:
Forecast of Study Success in the STEM Disciplines Based Solely on Academic Records. 647-657 - Mehdi Ali, Sahar Vahdati, Shruti Singh, Sourish Dasgupta, Jens Lehmann:
Improving Access to Science for Social Good. 658-673
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