31 Search Results for "Hammer, Barbara"

Document
DOI: 10.4230/LIPIcs.SoCG.2026.78
Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper

Authors: Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
The vast chemical space, encompassing virtually innumerable molecules and materials, presents both immense opportunities and significant challenges. The design and discovery of novel drugs and functional materials may be viewed as a search within this space; however, the sheer scale of potential candidates renders exhaustive exploration infeasible. To address this, we introduce Chemical Mapper, a framework that integrates topological data analysis with deep learning to enable the visual exploration and analysis of chemical latent spaces. At its core, Chemical Mapper employs mapper, a widely used tool in topological data analysis, to investigate the organizational principles of chemical latent spaces defined by molecular representations learned by geometric deep learning models. In doing so, Chemical Mapper not only highlights groups of molecular representations but also uncovers the relationships among them through linkages and branching structures. Our results show that Chemical Mapper reveals intrinsic patterns associated with molecular scaffolds, functional groups, and chemical properties, as well as the structural and functional evolutions of the molecules.
Cite as

Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang. Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 78:1-78:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{meduri_et_al:LIPIcs.SoCG.2026.78,
  author =	{Meduri, Dhruv and Hu, Chuan-Shen and Shen, Cong and Xia, Kelin and Wang, Bei},
  title =	{{Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{78:1--78:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.78},
  URN =		{urn:nbn:de:0030-drops-258854},
  doi =		{10.4230/LIPIcs.SoCG.2026.78},
  annote =	{Keywords: Practice of computational topology, topological data analysis, applications in chemistry, mapper algorithm, high-dimensional data analysis, chemical spaces, geometric deep learning, latent space geometry}
}
Document
DOI: 10.4230/LIPIcs.ITP.2025.14
Canonical for Automated Theorem Proving in Lean

Authors: Chase Norman and Jeremy Avigad

Published in: LIPIcs, Volume 352, 16th International Conference on Interactive Theorem Proving (ITP 2025)

Abstract
Canonical is a solver for type inhabitation in dependent type theory, that is, the problem of producing a term of a given type. We present a Lean tactic which invokes Canonical to generate proof terms and synthesize programs. The tactic supports higher-order and dependently-typed goals, structural recursion over indexed inductive types, and definitional equality. Canonical finds proofs for 84% of Natural Number Game problems in 51 seconds total.
Cite as

Chase Norman and Jeremy Avigad. Canonical for Automated Theorem Proving in Lean. In 16th International Conference on Interactive Theorem Proving (ITP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 352, pp. 14:1-14:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{norman_et_al:LIPIcs.ITP.2025.14,
  author =	{Norman, Chase and Avigad, Jeremy},
  title =	{{Canonical for Automated Theorem Proving in Lean}},
  booktitle =	{16th International Conference on Interactive Theorem Proving (ITP 2025)},
  pages =	{14:1--14:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-396-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{352},
  editor =	{Forster, Yannick and Keller, Chantal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITP.2025.14},
  URN =		{urn:nbn:de:0030-drops-246128},
  doi =		{10.4230/LIPIcs.ITP.2025.14},
  annote =	{Keywords: Automated Reasoning, Interactive Theorem Proving, Dependent Type Theory, Inhabitation, Unification, Program Synthesis, Formal Methods}
}
Document
Replication Paper
DOI: 10.4230/LIPIcs.ECOOP.2025.40
Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification (Replication Paper)

Authors: Francisco Handrick Tomaz da Costa, Ismael Medeiros, Leandro Oliveira, João Calássio, Rodrigo Bonifácio, Krishna Narasimhan, Mira Mezini, and Márcio Ribeiro

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The widespread use of smartphones in daily life has raised concerns about privacy and security among researchers and practitioners. Privacy issues are generally highly prevalent in mobile applications, particularly targeting the Android platform - the most popular mobile operating system. For this reason, several techniques have been proposed to identify malicious behavior in Android applications, including the Mining Android Sandbox approach (MAS approach), which aims to identify malicious behavior in repackaged Android applications (apps). However, previous empirical studies evaluated the MAS approach using a small dataset consisting of only 102 pairs of original and repackaged apps. This limitation raises questions about the external validity of their findings and whether the MAS approach can be generalized to larger datasets. To address these concerns, this paper presents the results of a replication study focused on evaluating the performance of the MAS approach regarding its capabilities of correctly classifying malware from different families. Unlike previous studies, our research employs a dataset that is an order of magnitude larger, comprising 4,076 pairs of apps covering a more diverse range of Android malware families. Surprisingly, our findings indicate a poor performance of the MAS approach for identifying malware, with the F1-score decreasing from 0.90 for the small dataset used in the previous studies to 0.54 in our more extensive dataset. Upon closer examination, we discovered that certain malware families partially account for the low accuracy of the MAS approach, which fails to classify a repackaged version of an app as malware correctly. Our findings highlight the limitations of the MAS approach, particularly when scaled, and underscore the importance of complementing it with other techniques to detect a broader range of malware effectively. This opens avenues for further discussion on addressing the blind spots that affect the accuracy of the MAS approach.
Cite as

Francisco Handrick Tomaz da Costa, Ismael Medeiros, Leandro Oliveira, João Calássio, Rodrigo Bonifácio, Krishna Narasimhan, Mira Mezini, and Márcio Ribeiro. Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification (Replication Paper). In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 40:1-40:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{handricktomazdacosta_et_al:LIPIcs.ECOOP.2025.40,
  author =	{Handrick Tomaz da Costa, Francisco and Medeiros, Ismael and Oliveira, Leandro and Cal\'{a}ssio, Jo\~{a}o and Bonif\'{a}cio, Rodrigo and Narasimhan, Krishna and Mezini, Mira and Ribeiro, M\'{a}rcio},
  title =	{{Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{40:1--40:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.40},
  URN =		{urn:nbn:de:0030-drops-233320},
  doi =		{10.4230/LIPIcs.ECOOP.2025.40},
  annote =	{Keywords: Android Malware Detection, Dynamic Analysis, Mining Android Sandboxes}
}
Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.12
A View on Vulnerabilites: The Security Challenges of XAI (Academic Track)

Authors: Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
Modern deep learning methods have long been considered as black-boxes due to their opaque decision-making processes. Explainable Artificial Intelligence (XAI), however, has turned the tables: it provides insight into how these models work, promoting transparency that is crucial for accountability. Yet, recent developments in adversarial machine learning have highlighted vulnerabilities in XAI methods, raising concerns about security, reliability and trustworthiness, particularly in sensitive areas like healthcare and autonomous systems. Awareness of the potential risks associated with XAI is needed as its adoption increases, driven in part by the need to enhance compliance to regulations. This survey provides a holistic perspective on the security and safety landscape surrounding XAI, categorizing research on adversarial attacks against XAI and the misuse of explainability to enhance attacks on AI systems, such as evasion and privacy breaches. Our contribution includes identifying current insecurities in XAI and outlining future research directions in adversarial XAI. This work serves as an accessible foundation and outlook to recognize potential research gaps and define future directions. It identifies data modalities, such as time-series or graph data, and XAI methods that have not been extensively investigated for vulnerabilities in current research.
Cite as

Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz. A View on Vulnerabilites: The Security Challenges of XAI (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 12:1-12:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pachl_et_al:OASIcs.SAIA.2024.12,
  author =	{Pachl, Elisabeth and Langer, Fabian and Markert, Thora and Lorenz, Jeanette Miriam},
  title =	{{A View on Vulnerabilites: The Security Challenges of XAI}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{12:1--12:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.12},
  URN =		{urn:nbn:de:0030-drops-227523},
  doi =		{10.4230/OASIcs.SAIA.2024.12},
  annote =	{Keywords: Explainability, XAI, Transparency, Adversarial Machine Learning, Security, Vulnerabilities}
}
Document
DOI: 10.4230/DagRep.14.5.1
Generalization by People and Machines (Dagstuhl Seminar 24192)

Authors: Barbara Hammer, Filip Ilievski, Sascha Saralajew, and Frank van Harmelen

Published in: Dagstuhl Reports, Volume 14, Issue 5 (2024)

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
DOI: 10.4230/DagRep.1.8.67
Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

Authors: Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman

Published in: Dagstuhl Reports, Volume 1, Issue 8 (2011)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 11341 "Learning in the context of very high dimensional data". The aim of the seminar was to bring together researchers who develop, investigate, or apply machine learning methods for very high dimensional data to advance this important field of research. The focus was be on broadly applicable methods and processing pipelines, which offer efficient solutions for high-dimensional data analysis appropriate for a wide range of application scenarios.
Cite as

Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman. Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). In Dagstuhl Reports, Volume 1, Issue 8, pp. 67-95, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@Article{biehl_et_al:DagRep.1.8.67,
  author =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  title =	{{Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)}},
  pages =	{67--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{8},
  editor =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.1.8.67},
  URN =		{urn:nbn:de:0030-drops-33125},
  doi =		{10.4230/DagRep.1.8.67},
  annote =	{Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization}
}
Document
DOI: 10.4230/DagSemProc.10302.1
10302 Abstracts Collection – Learning paradigms in dynamic environments

Authors: Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint. 10302 Abstracts Collection – Learning paradigms in dynamic environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.1,
  author =	{Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang and Toussaint, Marc},
  title =	{{10302 Abstracts Collection – Learning paradigms in dynamic environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.1},
  URN =		{urn:nbn:de:0030-drops-28048},
  doi =		{10.4230/DagSemProc.10302.1},
  annote =	{Keywords: Recurrent neural networks, Dynamic systems, Speech processing, Neurobiology, Neural-symbolic integration, Autonomous learning}
}
Document
DOI: 10.4230/DagSemProc.10302.2
10302 Summary – Learning paradigms in dynamic environments

Authors: Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web.
Cite as

Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint. 10302 Summary – Learning paradigms in dynamic environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.2,
  author =	{Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang and Toussaint, Marc},
  title =	{{10302 Summary – Learning paradigms in dynamic environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.2},
  URN =		{urn:nbn:de:0030-drops-28027},
  doi =		{10.4230/DagSemProc.10302.2},
  annote =	{Keywords: Summary}
}
Document
DOI: 10.4230/DagSemProc.10302.3
Neurons and Symbols: A Manifesto

Authors: Artur S. d'Avila Garcez

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty.
Cite as

Artur S. d'Avila Garcez. Neurons and Symbols: A Manifesto. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{davilagarcez:DagSemProc.10302.3,
  author =	{d'Avila Garcez, Artur S.},
  title =	{{Neurons and Symbols: A Manifesto}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.3},
  URN =		{urn:nbn:de:0030-drops-28005},
  doi =		{10.4230/DagSemProc.10302.3},
  annote =	{Keywords: Neuro-symbolic systems, cognitive models, machine learning}
}
Document
DOI: 10.4230/DagSemProc.10302.4
One-shot Learning of Poisson Distributions in fast changing environments

Authors: Peter Tino

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
In Bioinformatics, Audic and Claverie were among the first to systematically study the influence of random fluctuations and sampling size on the reliability of digital expression profile data. For a transcript representing a small fraction of the library and a large number N of clones, the probability of observing x tags of the same gene will be well-approximated by the Poisson distribution parametrised by its mean (and variance) m>0, where the unknown parameter m signifies the number of transcripts of the given type (tag) per N clones in the cDNA library. On an abstract level, to determine whether a gene is differentially expressed or not, one has two numbers generated from two distinct Poisson distributions and based on this (extremely sparse) sample one has to decide whether the two Poisson distributions are identical or not. This can be used e.g. to determine equivalence of Poisson photon sources (up to time shift) in gravitational lensing. Each Poisson distribution is represented by a single measurement only, which is, of course, from a purely statistical standpoint very problematic. The key instrument of the Audic-Claverie approach is a distribution P over tag counts y in one library informed by the tag count x in the other library, under the null hypothesis that the tag counts are generated from the same but unknown Poisson distribution. P is obtained by Bayesian averaging (infinite mixture) of all possible Poisson distributions with mixing proportions equal to the posteriors (given x) under the flat prior over m. We ask: Given that the tag count samples from SAGE libraries are *extremely* limited, how useful actually is the Audic-Claverie methodology? We rigorously analyse the A-C statistic P that forms a backbone of the methodology and represents our knowledge of the underlying tag generating process based on one observation. We show will that the A-C statistic P and the underlying Poisson distribution of the tag counts share the same mode structure. Moreover, the K-L divergence from the true unknown Poisson distribution to the A-C statistic is minimised when the A-C statistic is conditioned on the mode of the Poisson distribution. Most importantly (and perhaps rather surprisingly), the expectation of this K-L divergence never exceeds 1/2 bit! This constitutes a rigorous quantitative argument, extending the previous empirical Monte Carlo studies, that supports the wide spread use of Audic-Claverie method, even though by their very nature, the SAGE libraries represent very sparse samples.
Cite as

Peter Tino. One-shot Learning of Poisson Distributions in fast changing environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{tino:DagSemProc.10302.4,
  author =	{Tino, Peter},
  title =	{{One-shot Learning of Poisson Distributions in fast changing environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.4},
  URN =		{urn:nbn:de:0030-drops-27998},
  doi =		{10.4230/DagSemProc.10302.4},
  annote =	{Keywords: Audic-Claverie statistic, Bayesian averaging, information theory, one-shot learning, Poisson distribution}
}
Document
DOI: 10.4230/DagSemProc.10302.5
Some steps towards a general principle for dimensionality reduction mappings

Authors: Barbara Hammer, Kerstin Bunte, and Michael Biehl

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
In the past years, many dimensionality reduction methods have been established which allow to visualize high dimensional data sets. Recently, also formal evaluation schemes have been proposed for data visualization, which allow a quantitative evaluation along general principles. Most techniques provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers the possibility of simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based in a simple example.
Cite as

Barbara Hammer, Kerstin Bunte, and Michael Biehl. Some steps towards a general principle for dimensionality reduction mappings. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.5,
  author =	{Hammer, Barbara and Bunte, Kerstin and Biehl, Michael},
  title =	{{Some steps towards a general principle for dimensionality reduction mappings}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.5},
  URN =		{urn:nbn:de:0030-drops-28034},
  doi =		{10.4230/DagSemProc.10302.5},
  annote =	{Keywords: Visualization, dimensionality reduction}
}
Document
DOI: 10.4230/DagSemProc.10302.6
Why deterministic logic is hard to learn but Statistical Relational Learning works

Authors: Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
A brief note on why we think that the statistical relational learning framework is a great advancement over deterministic logic – in particular in the context of model-based Reinforcement Learning.
Cite as

Marc Toussaint. Why deterministic logic is hard to learn but Statistical Relational Learning works. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{toussaint:DagSemProc.10302.6,
  author =	{Toussaint, Marc},
  title =	{{Why deterministic logic is hard to learn but Statistical Relational Learning works}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.6},
  URN =		{urn:nbn:de:0030-drops-28014},
  doi =		{10.4230/DagSemProc.10302.6},
  annote =	{Keywords: Statistical relational learning, relational model-based Reinforcement Learning}
}
Document
DOI: 10.4230/DagSemProc.09081.1
09081 Abstracts Collection – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
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Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Abstracts Collection – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{biehl_et_al:DagSemProc.09081.1,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Abstracts Collection – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.1},
  URN =		{urn:nbn:de:0030-drops-20395},
  doi =		{10.4230/DagSemProc.09081.1},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
DOI: 10.4230/DagSemProc.09081.2
09081 Summary – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
The seminar centered around different aspects of similarity-based clustering with the special focus on structures. This included theoretical foundations, new algorithms, innovative applications, and future challenges for the field.
Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Summary – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{biehl_et_al:DagSemProc.09081.2,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Summary – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.2},
  URN =		{urn:nbn:de:0030-drops-20382},
  doi =		{10.4230/DagSemProc.09081.2},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
DOI: 10.4230/DagSemProc.09081.3
An adaptive model for learning molecular endpoints

Authors: Ian Walsh, Alessandro Vullo, and Gianluca Pollastri

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
I will describe a recursive neural network that deals with undirected graphs, and its application to predicting property labels or activity values of small molecules. The model is entirely general, in that it can process any undirected graph with a finite number of nodes by factorising it into a number of directed graphs with the same skeleton. The model's only input in the applications I will present is the graph representing the chemical structure of the molecule. In spite of its simplicity, the model outperforms or matches the state of the art in three of the four tasks, and in the fourth is outperformed only by a method resorting to a very problem-specific feature.
Cite as

Ian Walsh, Alessandro Vullo, and Gianluca Pollastri. An adaptive model for learning molecular endpoints. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{walsh_et_al:DagSemProc.09081.3,
  author =	{Walsh, Ian and Vullo, Alessandro and Pollastri, Gianluca},
  title =	{{An adaptive model for learning molecular endpoints}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.3},
  URN =		{urn:nbn:de:0030-drops-20367},
  doi =		{10.4230/DagSemProc.09081.3},
  annote =	{Keywords: Recursive neural networks, qsar, qspr, small molecules}
}
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