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2023ECMLPKDDworkshop.md

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Neuro-symbolic Metalearning and AutoML

Workshop co-hosted at ECML/PKDD 2023{:target="_blank" rel="noopener"}.

Date: September 18, 2023 (afternoon)

Location: PoliTo Room 4i

Invited Speakers

Program

  • 14:30 - 14:40 opening {:target="_blank" rel="noopener"}
  • 14:40 - 15:15 keynote by Bernhard Pfahringer: Learning from Data Streams versus Continual Learning {:target="_blank" rel="noopener"}
  • 15:15 - 15:35 selected paper presentation I: Zhivar Sourati Hassan Zadeh (Information Sciences Institute); Vishnu Priya Prasanna Venkatesh (USC/ISI); Darshan Deshpande (USC/ISI); Himanshu Rawlani (USC/ISI); Filip Ilievski (USC/ISI); Hông Ân Sandlin (Cyber-Defence Campus, armasuisse Science and Technology); Alain Mermoud (Cyber-Defence Campus, armasuisse Science and Technology): Robust and explainable identification of logical fallacies in natural language arguments {:target="_blank" rel="noopener"}
  • 15:35 - 15:55 selected paper presentation II: Katarzyna Woźnica (Warsaw University of Technology); Mateusz Grzyb (Warsaw University of Technology); Zuzanna Trafas (Poznan University of Technology); Przemyslaw Biecek (Warsaw University of Technology): Consolidated learning - a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks {:target="_blank" rel="noopener"}
  • 16:00 - 16:30 coffee break + poster session
  • 16:30 - 16:45 poster session (finish)
  • 16:45 - 17:20 keynote by Carlos Soares: Synthetic data for a better understanding of models and algorithms: GANs for stress testing and other methods {:target="_blank" rel="noopener"}
  • 17:20 - 17:55 keynote by Artur d'Avila Garcez: Neurosymbolic AI Contributions to Metalearning {:target="_blank" rel="noopener"}
  • 17:55 - 18:00 closing

List of Posters

  • Katarzyna Woźnica (Warsaw University of Technology); Mateusz Grzyb (Warsaw University of Technology); Zuzanna Trafas (Poznan University of Technology); Przemyslaw Biecek (Warsaw University of Technology): Consolidated learning - a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks
  • Kaixin Ma (Carnegie Mellon University); Filip Ilievski (USC/ISI); Jonathan M Francis (Carnegie Mellon University); Eric Nyberg (CMU); Alessandro Oltramari (Bosch Research Pittsburgh): Coalescing Global and Local Information for Procedural Text Understanding {:target="_blank" rel="noopener"}
  • Jiarui Zhang (USC/ISI); Filip Ilievski (USC/ISI); Kaixin Ma (CMU); Jonathan M Francis (Bosch Center for AI; Carnegie Mellon University); Alessandro Oltramari (Bosch Research Pittsburgh): A Study of Zero-shot Adaptation with Commonsense Knowledge {:target="_blank" rel="noopener"}
  • Mansour Sami (Edinburgh Napier University); ASHKAN SAMI (Edinburgh Napier University); Peter Barclay (Edinburgh Napier University): Unveiling the Boundaries: Diversity Guardrails in Generative AI and Their Limitations
  • Inês Gomes (University of Porto); Carlos Soares (University of Porto); Luis F Teixeira (INESC TEC and University of Porto); Jan N. van Rijn (Leiden University); André Restivo (University of Porto): Interpretable Generative Stress Testing
  • Fernando Freitas (University of Porto); Pavel Brazdil (INESC TEC); Carlos Soares (University of Porto): Exploring the Reduction of Configuration Spaces of Workflows {:target="_blank" rel="noopener"}
  • Lionel Kielhofer (Leiden University); Felix Mohr (Universidad de La Sabana); Jan N. van Rijn (Leiden University): Learning curve extrapolation techniques across extrapolation settings {:target="_blank" rel="noopener"}
  • Luísa B. Shimabucoro (University of Sao Paulo), Timothy M. Hospedales (University of Edinburgh) and Henry Gouk (University of Edinburgh): Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose? {:target="_blank" rel="noopener"}

Organization

General organizers / Program Chairs (ordered by last name)

  • Pavel Brazdil{:target="_blank" rel="noopener"}, University of Porto, Portugal
  • Henry Gouk{:target="_blank" rel="noopener"}, University of Edinburgh, Scotland
  • Jan N. van Rijn{:target="_blank" rel="noopener"}, Leiden University, The Netherlands
  • Md Kamruzzaman Sarker{:target="_blank" rel="noopener"}, Bowie State University, USA

Call For Papers

This workshop explores different types of meta-knowledge, such as performance summary statistics or pre-trained model weights. One way of acquiring meta-knowledge is by observing learning processes and representing it in such a way that it can be used later to improve future learning processes. AutoML systems typically explore meta-knowledge acquired from a single task, e.g., by modelling the relationship between hyperparameters and model performance. Metalearning systems, on the other hand, normally explore metaknowledge acquired on a collection of machine learning tasks. This can be used not only for selection of the best workflow(s) for the current task, but also for adaptation and fine-tuning of a prior model to the new task. Many current AutoML and metalearning systems exploit both types of meta-knowledge. Neuro-symbolic systems explore the interplay between neural network-based learning and symbol-based learning to get the best of those two types of learning. While doing so, it tries to use the existing knowledge as a concrete symbolic representation or as a transformed version of the symbolic representation suited for the learning algorithm. The goal of this workshop is to explore ways in which ideas can be cross-pollinated between the AutoML/Metalearning and neuro-symbolic learning research communities. This could lead to, e.g., systems with interpretable meta-knowledge, and tighter integration between machine learning workflows and automated reasoning systems.

Main research areas:

  • Controlling the learning processes
  • Definitions of configuration spaces
  • Few-shot learning
  • Elaboration of feature hierarchies
  • Exploiting hierarchy of features in learning
  • Meta-learning
  • Conditional meta-learning
  • Meta-knowledge transfer
  • Transfer learning
  • Transfer of prior models
  • Transfer of meta-knowledge between systems
  • Symbolic vs subsymbolic meta-knowledge
  • Neuro-symbolic learning
  • Explainable and interpretable meta-learning
  • Explainable artificial intelligence

Program Committee

  • Shikha Bordia (Verisk Analytics)
  • Kemilly Dearo
  • Hugo Jair Escalante(INAOE)
  • Eibe Frank (University of Waikato)
  • Joao Gama (INESC TEC - LIAAD)
  • Dagmar Gromann (University of Vienna)
  • Filip Ilievski (USC/ISI)
  • Adwaita Jadhav (Apple)
  • Pavel Kordík (Czech Technical University in Prague)
  • Lars Kotthoff (University of Wyoming)
  • Bo Liu (Auburn University)
  • Robin Manhaeve (KU Leuven)
  • Bernhard Pfahringer (University of Waikato)
  • Peter van der Putten (Leiden University)
  • Thalea Schlender (CWI, LUMC)
  • Martin Wistuba (Amazon)

Submission

This workshop hosts the following tracks:

  • Original paper track: Authors can submit novel papers, that have not been accepted elsewhere. Please format your submission according to the LaTeX Lecture Notes in Computer Science{:target="_blank" rel="noopener"} format, maximal 12 pages. (closed)
  • Poster of already published work: Authors can apply for a poster spot for a paper that has recently (less than 2 years) been published elsewhere. During submission, you send a link to the already published version of the work, and the peer-review will determine whether it is a good match based on the topic. (closed)
  • Late breaking papers: Authors can submit a 2-page abstract of already published work, or work to be published, that will undergo a light review process tailored towards applicability towards the workshop. The work will end up in the proceedings. (closed)

Submissions go through the Conference Management Tool{:target="_blank" rel="noopener"}, please ensure to select the right track: Neuro-symbolic Metalearning and AutoML.

Please use the template suggested by the organisation of ECML/PKDD{:target="_blank" rel="noopener"}

Format of the Workshop

The workshop will last a half a day. It will include:

  • Invited talks
  • Short oral presentations
  • Poster session
  • Panel discussions on "Neuro-symbolic Metalearning and AutoML"

Proceedings

Accepted papers can decide to opt-in to the formal workshop proceedings of ECML/PKDD 2023. The authors of accepted papers can decide whether they wish to have their full paper included or not. In the latter case, publication of a short abstract would be possible.

Important Dates

  • Workshop paper submission deadline: June 26, 2023 (updated)
  • Workshop paper author notification: July 24, 2023 (updated)
  • Camera ready deadline: End of July 2023
  • Late breaking papers submission deadline: August 31st, 2023
  • Workshop: September 18, 2023 (afternoon)