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Clustering: Science or art? Towards principled approaches
Margareta Ackerman · Shai Ben-David · Avrim Blum · Isabelle Guyon · Ulrike von Luxburg · Robert Williamson · Reza Zadeh

Fri Dec 11 07:30 AM -- 06:30 PM (PST) @ Hilton: Mt. Currie South
Event URL: http://clusteringtheory.org/ »

This workshop aims at initiating a dialog between theoreticians and practitioners, aiming to bridge the theory-practice gap in this area. The workshop will be built along three main question: FROM THEORY TO PRACTICE: Which abstract theoretical characterizations / properties / statements about clustering algorithms exist that can be helpful for practitioners and should be adopted in practice? FROM PRACTICE TO THEORY: What concrete questions would practitioners like to see addressed by theoreticians? Can we identify de-facto practices in clustering in need of theoretical grounding? Which obscure (but seemingly needed or useful) practices are in need of rationalization? FROM ART TO SCIENCE: In contrast to supervised learning, where there is general consensus on how to assess the quality of an algorithm, the frameworks for analyzing clustering are only beginning to be developed and clustering is still largely an art. How can we progress towards a deeper understanding of the space of clustering problems and objectives, including the introduction of falsifiable hypotheses and properly designed experimentation? How could one set up a clustering challenge to compare different clustering algorithms? What could be scientific standards to evaluate a clustering algorithm in a paper? The workshop will also serve as a follow up meeting to the NIPS 2005 “Theoretical Foundations of clustering” workshop, a venue for the different research groups working on these issues to take stock, exchange view points and discuss the next challenges in this ambitious quest for theoretical foundations of clustering.

Author Information

Margareta Ackerman (Florida State University)
Shai Ben-David (University of Waterloo)
Avrim Blum (Toyota Technological Institute at Chicago)
Isabelle Guyon (Google and ChaLearn)

Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.

Ulrike von Luxburg (University of Tuebingen)
Robert Williamson (Australian National University & Data61)
Reza Zadeh (Matroid and Stanford)

Reza Bosagh Zadeh is Founder CEO at Matroid and an Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks. As part of his research, Reza built the Machine Learning Algorithms behind Twitter's who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza's work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305).

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