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Trustworthy AI - Integrating Learning, Optimization and Reasoning : First International Workshop, TAILOR 2020, Virtual Event, September 4–5, 2020, Revised Selected Papers
Lab Conditions for Research on Explainable Automated Decisions
other
Author(s):
Christel Baier
,
Maria Christakis
,
Timo P. Gros
,
David Groß
,
Stefan Gumhold
,
Holger Hermanns
,
Jörg Hoffmann
,
Michaela Klauck
Publication date
(Online):
April 13 2021
Publisher:
Springer International Publishing
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Markov Decision Processes
Martin L Puterman
(1994)
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Learning to act using real-time dynamic programming
Andrew G Barto
,
Steven J. Bradtke
,
Satinder Singh
(1995)
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Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
Håkan L. S. Younes
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Reid G. Simmons
(2002)
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Book Chapter
Publication date (Print):
2021
Publication date (Online):
April 13 2021
Pages
: 83-90
DOI:
10.1007/978-3-030-73959-1_8
SO-VID:
05b9942d-5ac7-4e57-96be-a14aabbdecda
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Book chapters
pp. 3
Towards Automated GDPR Compliance Checking
pp. 20
Underestimation Bias and Underfitting in Machine Learning
pp. 32
Consensus for Collaborative Creation of Risk Maps for COVID-19
pp. 49
Guided-LORE: Improving LORE with a Focused Search of Neighbours
pp. 63
Interactive Natural Language Technology for Explainable Artificial Intelligence
pp. 71
Hybrid AI: The Way Forward in AI by Developing Four Dimensions
pp. 77
Towards Certifying Trustworthy Machine Learning Systems
pp. 83
Lab Conditions for Research on Explainable Automated Decisions
pp. 93
Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers
pp. 104
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms
pp. 112
An Analysis of Regularized Approaches for Constrained Machine Learning
pp. 123
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
pp. 140
A Causal Framework for Understanding Optimisation Algorithms
pp. 146
Uncertainty Quantification and Calibration of Imitation Learning Policy in Autonomous Driving
pp. 163
Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning
pp. 180
Modelling Agent Policies with Interpretable Imitation Learning
pp. 189
Value-Alignment Equilibrium in Multiagent Systems
pp. 205
Election Manipulation on Social Networks with Messages on Multiple Candidates Extended Abstract
pp. 212
Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language
pp. 220
AI-Supported Innovation Monitoring
pp. 229
Semi-supervised Co-ensembling for AutoML
pp. 251
Transparent Adaptation in Deep Medical Image Diagnosis
pp. 268
Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness
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