Seminar: Guest Lecture
Geometry-oriented Measures of Dependence
Date: June,01,2023 Start Time: 14:30 - 15:30
Location: 1061, Meyer Building
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Lecturer: Anatoly Khina
|One of the fundamental problems of statistics and data science is identifying and measuring dependence. This problem dates back to the works of Bravais, Galton, and Pearson in the 18th century on dependence measure design, and to the work of Rényi the late 1950s on axiomatizing the desired properties of such measures. For discrete random variables, categorical dependence measures—primarily those based on Shannon’s mutual information and entropy, and maximal correlation—are valid choices when only the information content is important.
However, when some possible underlying physical interpretation is of the essence, other measures need to be sought after. Consequently, much effort has been put into both the design and property axiomatization of such dependence measures, when the dependence strength is dictated by the inference quality with respect to some metric. In this talk, I will first propose a new set of natural axioms that reflect desired innate geometric properties. I will show that, in fact, none of the existing dependence measures satisfies this set of axioms and has a known feasible evaluation algorithm. Finally, I will propose a new computationally efficient dependence measure that satisfies all the proposed axioms and compare its performance to that of classical dependence measures such as maximal correlation and correlation ratio, as well as recently proposed measures such as xicor (Chatterjee JASA ‘21), distance correlation (Székely et al. Ann. Stat. ‘07), and maximal information coefficient (Reshef et al. Science ‘11).Joint work with Elad Domanovitz and Yoad Nitzan.
|Anatoly Khina is a faculty member in the School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel, from which he holds B.Sc. (2006), M.Sc. (2010), and Ph.D. (2016) degrees. Parallel to his studies, he worked as an engineer in various roles focused on algorithms, data science, software and hardware. He was a Postdoctoral Scholar in the Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA, from 2015 to 2018, and a Research Fellow at the Simons Institute for the Theory of Computing, University of California, Berkeley, Berkeley, CA, USA, during the spring of 2018. His research interests include Information Theory, Control Theory, Signal Processing and Statistics.|