September | 2022 | Statistical Learning Reading Group

Statistical Learning reading group will meet this semester after a long hiatus! We will meet from 11:30am to 12:30 pm in Cockins Hall Room 212 every other Tuesday starting from September 6th. Students may register for course credit by enrolling in STAT 8750.01. If you are not officially enrolled but want to be added to the reading group email list, please contact Haozhen Yu at yu.2823@osu.edu.

September 6: Welcome, organization, and discussion of
Prediction, estimation, and attribution by Brad Efron (2020);
Watch his recorded presentation for the 62nd ISI World Statistics Congress in Kuala Lumpur
September 20: Torey Hilbert and Xuerong Wang will discuss Sections 1-3 of the paper: Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation by Mikhail Belkin (2021)
October 4: Haozhen Yu and Zhenbang Jiao will discuss Sections 4-6 of the paper: Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation by Mikhail Belkin (2021)
October 18: Lun Li and Ningyi Liu will discuss Sections 1-2.2.1 of the following paper focusing on the concept of kernels: Kernel methods in machine learning by Thomas Hofmann, Bernhard Schölkopf, and Alexander J. Smola (2008)
November 1: Yue Ma and Jiahao Ping will discuss the paper: Deep neural networks as Gaussian processes by Jaehoon Lee, Yasaman Bahri, Roman Novak , Samuel S. Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein (2018)
November 15: Chenze Li and Cheng-Yu Hung will discuss the paper: Wide neural networks of any depth evolve as linear models under gradient descent by Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington (2019)
November 29: Arko Gupta and Nick Mandarano will discuss the following papers on word embedding: Distributed representations of words and phrases and their compositionality by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean (2013) and Efficient estimation of word representations in vector space by Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean (2013).

Time and Location: September 13 (Tuesday) 11:30am-12:30pm in CH 212

Speaker: Arnab Auddy (Columbia University)

Title: Why and how to use orthogonally decomposable tensors for statistical learning

Abstract: As we encounter more and more complex data generating mechanisms, it becomes necessary to model higher order interactions among the observed variables. Orthogonally decomposable tensors provide a unified framework for such modeling in a number of interesting statistical problems. While this is a natural extension of matrix SVD to tensors, they automatically provide much better identifiability properties. Moreover, a small perturbation affects each singular vector in isolation, and hence their recovery does not depend on the gap between consecutive singular values. In addition to the attractive statistical properties, the tensor decomposition problem in this case presents us with intriguing computational challenges. To understand these better, we will explore some statistical-computational tradeoffs, and also describe tractable methods that provide rate optimal estimators for the tensor singular vectors.

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Month: September 2022

2022 Autumn Reading

September 13: Seminar – Arnab Auddy