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Dagstuhl Reports, Volume 14, Issue 5
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Dagstuhl Reports, Volume 14
Journal: Dagstuhl Reports (DagRep)
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Dagstuhl Seminars 24192, 24201, 24202, 24211, 24212Publication Details
- published at: 2024-11-26
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Complete Issue
Dagstuhl Reports, Volume 14, Issue 5, May 2024, Complete Issue
Abstract
Dagstuhl Reports, Volume 14, Issue 5, May 2024, Complete Issue
Cite as
Dagstuhl Reports, Volume 14, Issue 5, pp. 1-192, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.14.5,
title = {{Dagstuhl Reports, Volume 14, Issue 5, May 2024, Complete Issue}},
pages = {1--192},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5},
URN = {urn:nbn:de:0030-drops-222699},
doi = {10.4230/DagRep.14.5},
annote = {Keywords: Dagstuhl Reports, Volume 14, Issue 5, May 2024, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 14, Issue 5, 2024
Abstract
Dagstuhl Reports, Table of Contents, Volume 14, Issue 5, 2024
Cite as
Dagstuhl Reports, Volume 14, Issue 5, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.14.5.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 14, Issue 5, 2024}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.i},
URN = {urn:nbn:de:0030-drops-222630},
doi = {10.4230/DagRep.14.5.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Generalization by People and Machines (Dagstuhl Seminar 24192)
Abstract
Today’s AI systems are powerful to the extent that they have largely entered the mainstream and divided the world between those who believe AI will solve all our problems and those who fear that AI will be destructive for humanity. Meanwhile, trusting AI is very difficult given its lack of robustness to novel situations, consistency of its outputs, and interpretability of its reasoning process. Building trustworthy AI requires a paradigm shift from the current oversimplified practice of crafting accuracy-driven models to a human-centric design that can enhance human ability on manageable tasks, or enable humans and AIs to solve complex tasks together that are difficult for either separately. At the core of this problem is the unrivaled human generalization and abstraction ability. While today’s AI is able to provide a response to any input, its ability to transfer knowledge to novel situations is still limited by oversimplification practices, as manifested by tasks that involve pragmatics, agent goals, and understanding of narrative structures. As there are currently no venues that allow cross-disciplinary research on the topic of reliable AI generalization, this discrepancy is problematic and requires dedicated efforts to bring in one place generalization experts from different fields within AI, but also with Cognitive Science. This Dagstuhl Seminar thus provided a unique opportunity for discussing the discrepancy between human and AI generalization mechanisms and crafting a vision on how to align the two streams in a compelling and promising way that combines the strengths of both. To ensure an effective seminar, we brought together cross-disciplinary perspectives across computer and cognitive science fields. Our participants included experts in Interpretable Machine Learning, Neuro-Symbolic Reasoning, Explainable AI, Commonsense Reasoning, Case-based Reasoning, Analogy, Cognitive Science, and Human-AI Teaming. Specifically, the seminar participants focused on the following questions: How can cognitive mechanisms in people be used to inspire generalization in AI? What Machine Learning methods hold the promise to enable such reasoning mechanisms? What is the role of data and knowledge engineering for AI and human generalization? How can we design and model human-AI teams that can benefit from their complementary generalization capabilities? How can we evaluate generalization in humans and AI in a satisfactory manner?
Cite as
Barbara Hammer, Filip Ilievski, Sascha Saralajew, and Frank van Harmelen. Generalization by People and Machines (Dagstuhl Seminar 24192). In Dagstuhl Reports, Volume 14, Issue 5, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{hammer_et_al:DagRep.14.5.1,
author = {Hammer, Barbara and Ilievski, Filip and Saralajew, Sascha and van Harmelen, Frank},
title = {{Generalization by People and Machines (Dagstuhl Seminar 24192)}},
pages = {1--11},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
editor = {Hammer, Barbara and Ilievski, Filip and Saralajew, Sascha and van Harmelen, Frank},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.1},
URN = {urn:nbn:de:0030-drops-222682},
doi = {10.4230/DagRep.14.5.1},
annote = {Keywords: Abstraction, Cognitive Science, Generalization, Human-AI Teaming, Interpretable Machine Learning, Neuro-Symbolic AI}
}
Document
Discrete Algorithms on Modern and Emerging Compute Infrastructure (Dagstuhl Seminar 24201)
Abstract
Inspired by three plenary talks by leading figures in the area of "Discrete algorithms on modern and emerging compute infrastructure" this Dagstuhl Seminar emphasized focus sessions and working groups to dive into this very versatile topic. Lively discussions between experts from academia, research laboratories, and industry yielded a number of promising prospects for follow-up activities. As always, Dagstuhl provided the perfect setting for this kind of scientific exchange.
Cite as
Kathrin Hanauer, Uwe Naumann, Alex Pothen, and Robert Schreiber. Discrete Algorithms on Modern and Emerging Compute Infrastructure (Dagstuhl Seminar 24201). In Dagstuhl Reports, Volume 14, Issue 5, pp. 12-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{hanauer_et_al:DagRep.14.5.12,
author = {Hanauer, Kathrin and Naumann, Uwe and Pothen, Alex and Schreiber, Robert},
title = {{Discrete Algorithms on Modern and Emerging Compute Infrastructure (Dagstuhl Seminar 24201)}},
pages = {12--24},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
editor = {Hanauer, Kathrin and Naumann, Uwe and Pothen, Alex and Schreiber, Robert},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.12},
URN = {urn:nbn:de:0030-drops-222672},
doi = {10.4230/DagRep.14.5.12},
annote = {Keywords: Combinatorial Scientific Computing, Discrete Algorithms, Graph Algorithms, High-Performance Computing}
}
Document
Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24202 "Causal Inference for Spatial Data Analytics", taking place at Schloss Dagstuhl between May 12superscript{th} and 17superscript{th}, 2024.
The ability to identify causal relationships in spatial data is increasingly important for designing effective policy interventions in environmental science, epidemiology, urban planning, and traffic management. Current spatial data analytic methods rely mainly on descriptive and predictive methods that lack explicit causal models. Spatial causal inference, i.e. causal inference with spatial information offers a promising tool to address this challenge by extending causal inference methodologies to spatial domains. However, this translation is challenging due to spatial effects that might violate fundamental assumptions of causal inference. Spatial causal inference is therefore still in its infancy, and there is a pressing need to accelerate its theoretical development and support its adoption with a well-grounded methodological toolset. To facilitate the necessary interdisciplinary exchange of ideas we convened the first Dagstuhl Seminar on Causal Inference for Spatial Data Analytics.
Cite as
Martin Tomko, Yanan Xin, and Jonas Wahl. Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202). In Dagstuhl Reports, Volume 14, Issue 5, pp. 25-57, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{tomko_et_al:DagRep.14.5.25,
author = {Tomko, Martin and Xin, Yanan and Wahl, Jonas},
title = {{Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202)}},
pages = {25--57},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
editor = {Tomko, Martin and Xin, Yanan and Wahl, Jonas},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.25},
URN = {urn:nbn:de:0030-drops-222668},
doi = {10.4230/DagRep.14.5.25},
annote = {Keywords: Spatial Causal Analysis, Spatial Causal Inference, Spatial Causal Discovery, Spatial Analysis, Spatial Data, Dagstuhl Seminar}
}
Document
Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24211, "Evaluation Perspectives of Recommender Systems: Driving Research and Education", which brought together 41 participants from 16 countries.
The seminar brought together distinguished researchers and practitioners from the recommender systems community, representing a range of expertise and perspectives. The primary objective was to address current challenges and advance the ongoing discourse on the evaluation of recommender systems. The participants' diverse backgrounds and perspectives on evaluation significantly contributed to the discourse on this subject.
The seminar featured eight presentations on current challenges in the evaluation of recommender systems. These presentations sparked the general discussion and facilitated the formation of groups around these topics. As a result, five working groups were established, each focusing on the following areas: theory of evaluation, fairness evaluation, best-practices for offline evaluations of recommender systems, multistakeholder and multimethod evaluation, and evaluating the long-term impact of recommender systems.
Cite as
Christine Bauer, Alan Said, and Eva Zangerle. Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211). In Dagstuhl Reports, Volume 14, Issue 5, pp. 58-172, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{bauer_et_al:DagRep.14.5.58,
author = {Bauer, Christine and Said, Alan and Zangerle, Eva},
title = {{Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)}},
pages = {58--172},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
editor = {Bauer, Christine and Said, Alan and Zangerle, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.58},
URN = {urn:nbn:de:0030-drops-222655},
doi = {10.4230/DagRep.14.5.58},
annote = {Keywords: Recommender Systems, Evaluation, Information Retrieval, User Interaction, Intelligent Systems}
}
Document
Classical-Quantum Synergies in the Theory and Practice of Quantum Error Correction (Dagstuhl Seminar 24212)
Abstract
The Dagstuhl Seminar 24212 "Classical-Quantum Synergies in the Theory and Practice of Quantum Error Correction" was held on May 20-23, 2024, and brought together 30 participants from 13 countries. The seminar served as an interaction forum for senior and talented junior researchers, crossing boundaries between classical and quantum coding theory, and related areas of quantum technology and engineering problems. The topics covered by the seminar ranged from models of quantum noise to the theory and practice of quantum codes, including fault-tolerant error correction and fault-tolerant quantum computation, quantum error correction for specific technology constraints or noise models, decoding aspects of topological and quantum LDPC codes, and quantum error correction for scalable modular quantum computing architectures. The two and a half day program of the seminar consisted of 14 invited talks, and five breakout sessions, aimed at fostering an exchange of knowledge and viewpoints on challenges faced by quantum error correction. This report briefly presents the background, the motivation, and the topics covered by the seminar, and provides an overview of the invited talks and of three of the breakout sessions that brought together a large number of participants.
Cite as
Carmen G. Almudéver, Leonid Pryadko, Valentin Savin, and Bane Vasic. Classical-Quantum Synergies in the Theory and Practice of Quantum Error Correction (Dagstuhl Seminar 24212). In Dagstuhl Reports, Volume 14, Issue 5, pp. 173-190, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{almudever_et_al:DagRep.14.5.173,
author = {Almud\'{e}ver, Carmen G. and Pryadko, Leonid and Savin, Valentin and Vasic, Bane},
title = {{Classical-Quantum Synergies in the Theory and Practice of Quantum Error Correction (Dagstuhl Seminar 24212)}},
pages = {173--190},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {14},
number = {5},
editor = {Almud\'{e}ver, Carmen G. and Pryadko, Leonid and Savin, Valentin and Vasic, Bane},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.173},
URN = {urn:nbn:de:0030-drops-222647},
doi = {10.4230/DagRep.14.5.173},
annote = {Keywords: Fault-tolerant quantum computing, Quantum computing architectures, Quantum error correction, quantum information, Quantum LDPC codes}
}