• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 24 matches

• 2021
• 
  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 [contents]
• 2020
• 
  Michele Lombardi, Federico Baldo, Andrea Borghesi, Michela Milano:
  An Analysis of Regularized Approaches for Constrained Machine Learning. TAILOR 2020: 112-119
• 
  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. TAILOR 2020: 63-70
• 
  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. TAILOR 2020: 268-275
• 
  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. TAILOR 2020: 83-90
• 
  Tom Bewley, Jonathan Lawry, Arthur Richards:
  Modelling Agent Policies with Interpretable Imitation Learning. TAILOR 2020: 180-186
• 
  Barteld Braaksma, Piet Daas, Stephan Raaijmakers, Amber Geurts, André Meyer-Vitali:
  AI-Supported Innovation Monitoring. TAILOR 2020: 220-226
• 
  Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti, Giulia Landriani:
  Election Manipulation on Social Networks with Messages on Multiple Candidates Extended Abstract. TAILOR 2020: 205-211
• 
  Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker, Ann Nowé:
  Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning. TAILOR 2020: 163-179
• 
  Pádraig Cunningham, Sarah Jane Delany:
  Underestimation Bias and Underfitting in Machine Learning. TAILOR 2020: 20-31
• 
  Jesper E. van Engelen, Holger H. Hoos:
  Semi-supervised Co-ensembling for AutoML. TAILOR 2020: 229-250
• 
  Yago Fontenla-Seco, Manuel Lama, Alberto Bugarín:
  Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language. TAILOR 2020: 212-219
• 
  Alberto Franzin, Thomas Stützle:
  A Causal Framework for Understanding Optimisation Algorithms. TAILOR 2020: 140-145
• 
  Albert Huizing, Cor J. Veenman, Mark A. Neerincx, Judith Dijk:
  Hybrid AI: The Way Forward in AI by Developing Four Dimensions. TAILOR 2020: 71-76
• 
  Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez:
  Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art. TAILOR 2020: 123-139
• 
  Dimitris Kollias, Y. Vlaxos, M. Seferis, Ilianna Kollia, Levon Sukissian, James Wingate, Stefanos D. Kollias:
  Transparent Adaptation in Deep Medical Image Diagnosis. TAILOR 2020: 251-267
• 
  Tomer Libal:
  Towards Automated GDPR Compliance Checking. TAILOR 2020: 3-19
• 
  Najlaa Maaroof, Antonio Moreno, Aïda Valls, Mohammed Jabreel:
  Guided-LORE: Improving LORE with a Focused Search of Neighbours. TAILOR 2020: 49-62
• 
  Nieves Montes, Carles Sierra:
  Value-Alignment Equilibrium in Multiagent Systems. TAILOR 2020: 189-204
• 
  Farzad Nozarian, Christian Müller, Philipp Slusallek:
  Uncertainty Quantification and Calibration of Imitation Learning Policy in Autonomous Driving. TAILOR 2020: 146-162
• 
  Stefan Pócos, Iveta Becková, Tomás Kuzma, Igor Farkas:
  Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers. TAILOR 2020: 93-103
• 
  Fredrik Präntare, Mattias Tiger, David Bergström, Herman Appelgren, Fredrik Heintz:
  Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms. TAILOR 2020: 104-111
• 
  Miguel Rebollo, Carlos Carrascosa, Alberto Palomares:
  Consensus for Collaborative Creation of Risk Maps for COVID-19. TAILOR 2020: 32-48
• 
  Roland H. C. Yap:
  Towards Certifying Trustworthy Machine Learning Systems. TAILOR 2020: 77-82