Neuro-symbolic Metalearning and AutoML
Workshop co-hosted at ECML/PKDD 2023.
Date: September 18, 2023 (afternoon)
Location: PoliTo Room 4i
Invited Speakers
- Carlos Soares, University of Porto, Portugal
- Artur d’Avila Garcez, City University of London, UK
- Bernhard Pfahringer, University of Waikato, New Zealand
Program
- 14:30 - 14:40 opening
- 14:40 - 15:15 keynote by Bernhard Pfahringer: Learning from Data Streams versus Continual Learning
- 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
- 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
- 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
- 17:20 - 17:55 keynote by Artur d’Avila Garcez: Neurosymbolic AI Contributions to Metalearning
- 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
- 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
- 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
- Lionel Kielhofer (Leiden University); Felix Mohr (Universidad de La Sabana); Jan N. van Rijn (Leiden University): Learning curve extrapolation techniques across extrapolation settings
- 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?
Organization
General organizers / Program Chairs (ordered by last name)
- Pavel Brazdil, University of Porto, Portugal
- Henry Gouk, University of Edinburgh, Scotland
- Jan N. van Rijn, Leiden University, The Netherlands
- Md Kamruzzaman Sarker, 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 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, please ensure to select the right track: Neuro-symbolic Metalearning and AutoML
.
Please use the template suggested by the organisation of ECML/PKDD
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)