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ORCIDPKDD/ECML 2021: Bilbao, Spain (Virtual Event) - Workshops
- Michael Kamp
, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5
Advances in Interpretable Machine Learning and Artificial Intelligence
- Udo Schlegel, Duy Vo Lam, Daniel A. Keim, Daniel Seebacher:
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models. 5-14 - Troy Maasland, João Pereira, Diogo Bastos, Marcus de Goffau, Max Nieuwdorp, Aeilko H. Zwinderman, Evgeni Levin:
Interpretable Models via Pairwise Permutations Algorithm. 15-25 - Véronne Yepmo Tchaghe, Grégory Smits, Olivier Pivert:
A Classification of Anomaly Explanation Methods. 26-33 - Andrew Yeh, Anhthy Ngo:
Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots. 34-48 - Maxence Queyrel, Alexandre Templier, Jean-Daniel Zucker:
Reject and Cascade Classifier with Subgroup Discovery for Interpretable Metagenomic Signatures. 49-66 - Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl, Thomas A. Runkler:
Demystifying Graph Neural Network Explanations. 67-75 - Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro:
On the Transferability of Neural Models of Morphological Analogies. 76-89 - Chhavi Yadav, Kamalika Chaudhuri:
Behavior of k-NN as an Instance-Based Explanation Method. 90-96 - Hamed Behzadi Khormuji, Habib Rostami:
Enhancing Performance of Occlusion-Based Explanation Methods by a Hierarchical Search Method on Input Images. 97-104 - Victor Guyomard, Françoise Fessant, Tassadit Bouadi, Thomas Guyet:
Post-hoc Counterfactual Generation with Supervised Autoencoder. 105-114
Parallel, Distributed, and Federated Learning
- Timon Sachweh, Daniel Boiar, Thomas Liebig:
Differentially Private Learning from Label Proportions. 119-127 - Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer:
Approaches to Uncertainty Quantification in Federated Deep Learning. 128-145 - Yongli Mou, Jiahui Geng, Sascha Welten, Chunming Rong, Stefan Decker, Oya Beyan:
Optimized Federated Learning on Class-Biased Distributed Data Sources. 146-158 - Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu:
Splitting Algorithms for Federated Learning. 159-176 - Péter Kiss, Tomás Horváth:
Migrating Models: A Decentralized View on Federated Learning. 177-191
Graph Embedding and Mining
- Tobias Schumacher, Hinrikus Wolf, Martin Ritzert, Florian Lemmerich, Martin Grohe, Markus Strohmaier:
The Effects of Randomness on the Stability of Node Embeddings. 197-215 - Paul Beaujean, Florian Sikora, Florian Yger:
Graph Homomorphism Features: Why Not Sample? 216-222 - Thomas Pontoizeau, Florian Sikora, Florian Yger, Tristan Cazenave:
Neural Maximum Independent Set. 223-237 - Jiaqing Xie, Rex Ying:
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks. 238-257 - Chen Dang, Hicham Randrianarivo, Raphaël Fournier-S'niehotta, Nicolas Audebert:
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM Tree. 258-267 - Tomas Martin, Victor Fuentes, Petko Valtchev, Abdoulaye Baniré Diallo, René Lacroix, Maxime Leduc, Mounir Boukadoum:
Towards Mining Generalized Patterns from RDF Data and a Domain Ontology. 268-278
Machine Learning for Irregular Time Series
- Luciano Melodia, Richard Lenz:
Homological Time Series Analysis of Sensor Signals from Power Plants. 283-299 - Mona Schirmer, Mazin Eltayeb, Maja Rudolph:
Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series. 300-305 - Ankur Debnath, Nitish Gupta, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora:
Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data. 306-321
IoT, Edge, and Mobile for Embedded Machine Learning
- Lukas Einhaus, Chao Qian, Christopher Ringhofer, Gregor Schiele:
Towards Precomputed 1D-Convolutional Layers for Embedded FPGAs. 327-338 - Iris Walter, Jonas Ney, Tim Hotfilter, Vladimir Rybalkin, Julian Höfer, Norbert Wehn, Jürgen Becker:
Embedded Face Recognition for Personalized Services in the Assistive Robotics. 339-350 - Hassan Ghasemzadeh Mohammadi, Felix Paul Jentzsch, Maurice Kuschel, Rahil Arshad, Sneha Rautmare, Suraj Manjunatha, Marco Platzner, Alexander Boschmann, Dirk Schollbach:
FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics. 351-362 - Ilja van Ipenburg, Dolly Sapra, Andy D. Pimentel:
Exploring Cell-Based Neural Architectures for Embedded Systems. 363-374 - Armin Schuster, Christian Heidorn, Marcel Brand, Oliver Keszöcze, Jürgen Teich:
Design Space Exploration of Time, Energy, and Error Rate Trade-offs for CNNs Using Accuracy-Programmable Instruction Set Processors. 375-389 - Sven Nitzsche, Moritz Neher, Stefan von Dosky, Jürgen Becker:
Ultra-low Power Machinery Fault Detection Using Deep Neural Networks. 390-396 - Lukas Sommer, Cristian Axenie, Andreas Koch:
SPNC: Fast Sum-Product Network Inference. 397-408 - Bernhard Klein, Lisa Kuhn, Johannes Weis, Arne Emmel, Yannik Stradmann, Johannes Schemmel, Holger Fröning:
Towards Addressing Noise and Static Variations of Analog Computations Using Efficient Retraining. 409-420
eXplainable Knowledge Discovery in Data Mining
- Andreas Holzinger:
The Next Frontier: AI We Can Really Trust. 427-440 - Meike Nauta, Annemarie Jutte, Jesper C. Provoost, Christin Seifert:
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition. 441-456 - Kamil Plucinski, Mateusz Lango, Jerzy Stefanowski:
Prototypical Convolutional Neural Network for a Phrase-Based Explanation of Sentiment Classification. 457-472 - Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explanations for Network Embedding-Based Link Predictions. 473-488 - Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán:
Exploring Counterfactual Explanations for Classification and Regression Trees. 489-504 - Katarzyna Woznica, Przemyslaw Biecek:
Towards Explainable Meta-learning. 505-520 - Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki:
How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. 521-533 - Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana:
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. 534-551
Bias and Fairness in AI
- William Blanzeisky, Pádraig Cunningham:
Algorithmic Factors Influencing Bias in Machine Learning. 559-574 - Carlos Mougan Navarro, Georgios Kanellos, Thomas Gottron:
Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank. 575-590 - Serafina Kamp, Andong Luis Li Zhao, Sindhu Kutty:
Robustness of Fairness: An Experimental Analysis. 591-606 - Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet:
Co-clustering for Fair Recommendation. 607-630 - Daphne Lenders, Toon Calders:
Learning a Fair Distance Function for Situation Testing. 631-646 - Alessandro Castelnovo, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Cosentini:
Towards Fairness Through Time. 647-663
International Workshop on Active Inference
- Aswin Paul, Noor Sajid, Manoj Gopalkrishnan, Adeel Razi:
Active Inference for Stochastic Control. 669-680 - Mohamed Baioumy, Corrado Pezzato, Carlos Hernández Corbato, Nick Hawes, Riccardo M. G. Ferrari:
Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference. 681-691 - Ajith Anil Meera, Martijn Wisse:
On the Convergence of DEM's Linear Parameter Estimator. 692-700 - Toon Van de Maele, Tim Verbelen, Ozan Çatal, Bart Dhoedt:
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference. 701-714 - Tore Erdmann, Christoph Mathys:
Rule Learning Through Active Inductive Inference. 715-725 - Nathaniel Virgo, Martin Biehl, Simon McGregor:
Interpreting Dynamical Systems as Bayesian Reasoners. 726-762 - Morten Henriksen:
Blankets All the Way up - the Economics of Active Inference. 763-771 - Ben White, Mark Miller:
Filtered States: Active Inference, Social Media and Mental Health. 772-783 - Natalie Kastel, Casper Hesp:
Ideas Worth Spreading: A Free Energy Proposal for Cumulative Cultural Dynamics. 784-798 - Adam Safron, Zahra Sheikhbahaee:
Dream to Explore: 5-HT2a as Adaptive Temperature Parameter for Sophisticated Affective Inference. 799-809 - Peter Thestrup Waade, Nace Mikus, Christoph Mathys:
Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics. 810-818 - Mohamed Baioumy, Bruno Lacerda, Paul Duckworth, Nick Hawes:
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference. 819-829 - Paul F. Kinghorn, Beren Millidge, Christopher L. Buckley:
Habitual and Reflective Control in Hierarchical Predictive Coding. 830-842 - Niels van Hoeffelen, Pablo Lanillos:
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem. 843-856 - Daniel Burghardt, Pablo Lanillos:
Robot Localization and Navigation Through Predictive Processing Using LiDAR. 857-864 - Kanako Esaki, Tadayuki Matsumura, Kiyoto Ito, Hiroyuki Mizuno:
Sensorimotor Visual Perception on Embodied System Using Free Energy Principle. 865-877
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