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ORCIDTAILOR 2020: Virtual Event
- Fredrik Heintz
, Michela Milano, Barry O'Sullivan:
Trustworthy AI - Integrating Learning, Optimization and Reasoning - First International Workshop, TAILOR 2020, Virtual Event, September 4-5, 2020, Revised Selected Papers. Lecture Notes in Computer Science 12641, Springer 2021, ISBN 978-3-030-73958-4
Trustworthy AI
- Tomer Libal:
Towards Automated GDPR Compliance Checking. 3-19 - Pádraig Cunningham, Sarah Jane Delany:
Underestimation Bias and Underfitting in Machine Learning. 20-31 - Miguel Rebollo, Carlos Carrascosa, Alberto Palomares:
Consensus for Collaborative Creation of Risk Maps for COVID-19. 32-48 - Najlaa Maaroof, Antonio Moreno, Aïda Valls, Mohammed Jabreel:
Guided-LORE: Improving LORE with a Focused Search of Neighbours. 49-62 - José Maria Alonso, Senén Barro, Alberto Bugarín, Kees van Deemter, Claire Gardent, Albert Gatt, Ehud Reiter, Carles Sierra, Mariët Theune, Nava Tintarev, Hitoshi Yano, Katarzyna Budzynska:
Interactive Natural Language Technology for Explainable Artificial Intelligence. 63-70 - Albert Huizing, Cor J. Veenman, Mark A. Neerincx, Judith Dijk:
Hybrid AI: The Way Forward in AI by Developing Four Dimensions. 71-76 - Roland H. C. Yap:
Towards Certifying Trustworthy Machine Learning Systems. 77-82 - Christel Baier, Maria Christakis, Timo P. Gros, David Groß, Stefan Gumhold, Holger Hermanns, Jörg Hoffmann, Michaela Klauck:
Lab Conditions for Research on Explainable Automated Decisions. 83-90
Paradigms
- Stefan Pócos, Iveta Becková, Tomás Kuzma, Igor Farkas:
Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers. 93-103 - Fredrik Präntare, Mattias Tiger, David Bergström, Herman Appelgren, Fredrik Heintz:
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms. 104-111 - Michele Lombardi, Federico Baldo, Andrea Borghesi, Michela Milano:
An Analysis of Regularized Approaches for Constrained Machine Learning. 112-119
Acting and Optimizing
- Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez:
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art. 123-139 - Alberto Franzin, Thomas Stützle:
A Causal Framework for Understanding Optimisation Algorithms. 140-145 - Farzad Nozarian, Christian Müller, Philipp Slusallek:
Uncertainty Quantification and Calibration of Imitation Learning Policy in Autonomous Driving. 146-162 - Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker, Ann Nowé:
Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning. 163-179 - Tom Bewley, Jonathan Lawry, Arthur Richards:
Modelling Agent Policies with Interpretable Imitation Learning. 180-186
Social
- Nieves Montes, Carles Sierra:
Value-Alignment Equilibrium in Multiagent Systems. 189-204 - Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti, Giulia Landriani:
Election Manipulation on Social Networks with Messages on Multiple Candidates Extended Abstract. 205-211 - Yago Fontenla-Seco, Manuel Lama, Alberto Bugarín:
Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language. 212-219 - Barteld Braaksma, Piet Daas, Stephan Raaijmakers, Amber Geurts, André Meyer-Vitali:
AI-Supported Innovation Monitoring. 220-226
AutoAI
- Jesper E. van Engelen, Holger H. Hoos:
Semi-supervised Co-ensembling for AutoML. 229-250 - Dimitris Kollias, Y. Vlaxos, M. Seferis, Ilianna Kollia, Levon Sukissian, James Wingate, Stefanos D. Kollias:
Transparent Adaptation in Deep Medical Image Diagnosis. 251-267 - Mohammadreza Amirian, Lukas Tuggener, Ricardo Chavarriaga, Yvan Putra Satyawan, Frank-Peter Schilling, Friedhelm Schwenker, Thilo Stadelmann:
Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness. 268-275
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