Keynote Speakers

Title: TBC

Ophir Frieder

Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
Georgetown University, USA


Title: TBC

Kevin Leyton-Brown

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the Association of Computing Machinery (ACM)
University of British Columbia, Canada


Title: TBC

Ming Li

Fellow of the Royal Society of Canada, Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
University of Waterloo, Canada


Title: Green Machine Learning and Granular Modeling: Fostering New Development Avenues

Witold Pedrycz

Fellow of the Royal Society of Canada, Fellow of the Institute of Electrical and Electronics Engineering (IEEE)
University of Alberta, Canada


Abstract: The visible trends of Machine Learning (ML) are inherently associated with the diversity of data and innovative ways they are used in order to carry out learning pursuits. The ongoing objectives of the research agenda are also investigated in the context of green ML (usually referred to as green AI). One can identify three ongoing challenges with far-reaching methodological implications, namely (i)completing designs in the presence of strict constraints of privacy and security, (ii) efficient model building completed with limited data of varying quality, and (iii) a reduction of computing effort knowledge transfer and distillation.

We advocate that to conveniently address these quests, it becomes beneficial to engage the fundamental framework of Granular Computing to enhance the existing approaches (such as e.g., federated learning in case of (i) and transfer knowledge in (iii)) or establish new directions to the problem formulation. Likewise, it is also essential to establish sound mechanisms of evaluation of the performance of the ML architectures. It will be demonstrated that various ways of conceptualization of information granules in terms of fuzzy sets, sets, rough sets, and others may lead to efficient solutions.

To establish a suitable conceptual ML framework, we include a brief discussion of concepts of information granules and Granular Computing. We show how granular models endow numeric models with their quantification mechanisms.

To proceed with a detailed discussion, a concise information granules-oriented design of rule-based architectures is outlined. A way of forming the rules through unsupervised federated learning is investigated along with algorithmic developments. A granular characterization of the model formed by the server vis-a-vis data located at individual clients is presented. It is demonstrated that the quality of the rules at the client’s end is described in terms of granular parameters and subsequently the global model becomes represented as a granular construct. The roles of granular augmentations of models in the setting of granular knowledge distillation are outlined. It is shown how the agenda of green ML is effectively realized by exploring information granules and stressing an importance of the holistic perspective at critical trade-offs among interpretability, enormous computational overhead, and transparency of predictors and classifiers.


Title: TBC

Yiyu Yao

Fellow of the International Rough Set Society (IRSS)
University of Regina, Canada



Highlighted Keynote Speakers in the Past WI-IAT Editions



Edward Feigenbaum (Turing Award Laureate)   WI-IAT 2001, WI-IAT 2012
Lotfi A. Zadeh   WI-IAT 2003
John McCarthy (Turing Award Laureate)   WI-IAT 2004
Tom M. Mitchell   WI-IAT 2004, WI-IAT 2021
Richard M. Karp (Turing Award Laureate)   WI-IAT 2007
Yuichiro Anzai   WI-IAT 2011
John Hopcroft (Turing Award Laureate)   WI-IAT 2013
Andrew Chi-Chih Yao (Turing Award Laureate)   WI-IAT 2014
Joseph Sifakis (Turing Award Laureate)   WI-IAT 2015, WI-IAT 2021
Butler Lampson (Turing Award Laureate)   WI 2016
Leslie Valiant (Turing Award Laureate)   WI 2016, WI-IAT 2021
Raj Reddy (Turing Award Laureate)   WI 2017
Frank van Harmelon   WI-IAT 2021
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