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Geometrical Insights for Unsupervised Learning
Leon Bottou

Sat Dec 09 01:40 PM -- 02:20 PM (PST) @
in Optimal Transport and Machine Learning »

After arguing that choosing the right probability distance is critical for achieving the elusive goals of unsupervised learning, we compare the geometric properties of the two currently most promising distances: (1) the earth-mover distance, and (2) the energy distance, also known as maximum mean discrepancy. These insights allow us to give a fresh viewpoint on reported experimental results and to risk a couple predictions. Joint work with Leon Bottou, Martin Arjovsky, David Lopez-Paz, and Maxime Oquab.

Author Information

Leon Bottou (Facebook AI Research)

Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .

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