Sparse Estimation: Closing the Gap Between L0 and L1 Models

Aug 17, 2022 1:45 PM Singapore (Registration will open at 01:35 PM.)

Join Zoom Meeting:
https://sutd-edu-sg.zoom.us/j/87241603473?pwd=bytMckRPTDhTZS9XcnZoSEw3aWo0dz09

Meeting ID: 872 4160 3473
Passcode: 740248

Abstract

In this talk, we focus on two estimation problems: i) sparse regression and ii) sparse and smooth signal recovery. The first one is known to be NP-hard; we show that the second one is equivalent to a submodular minimization problem and, hence, it is polynomially solvable. For both problems, we derive a sequence of strong conic relaxations. The proposed rank-one strengthening can be interpreted as a non-separable, non-convex, unbiased sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the estimation error function without inducing bias for the sparse solutions. Computational experiments with benchmark datasets show that the proposed conic formulations are solved fast and result in near-optimal estimators for non-convex L0-problems. Moreover, the resulting estimators also outperform L1 approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.

This talk is based on the following papers with Andres Gomez & Shaoning Han:
https://atamturk.ieor.berkeley.edu/pubs/rank-one.pdf
http://proceedings.mlr.press/v119/atamturk20a/atamturk20a.pdf
https://jmlr.org/papers/volume22/18-745/18-745.pdf

About the Speaker

Alper Atamturk is the Earl J. Isaac Chair in the Science and Analysis of Decision Making, Professor and Chair of the Department of Industrial Engineering and Operations at the University of California, Berkeley. He received his Ph.D. from the Georgia Institute of Technology in 1998 with a major in Operations Research and minor in Computer Science. His research interests are in optimization, integer programming, optimization under uncertainty with applications to machine learning, energy systems, portfolio and network design.

He is the current chair of the INFORMS Optimization Society and UC Berkeley site director of the NSF AI Institute for Advances in Optimization. He serves as co-editor for Mathematical Programming, associate editor for Discrete Optimization, Mathematical Programming Computation, and Journal of Risk. Previously, he served on the editorial boards of Operations Research, Management Science, and Networks. He served on the organizing committees of INFORMS 2021, IPCO 2020, INFORMS 2014, IPCO 2010, MIP 2009, INFORMS 2008, MIP 2005, among others. He is a Fellow of INFORMS and Vannevar Bush Fellow of the US Department of Defense.

For more information about the ESD Seminar, please email esd_invite@sutd.edu.sg

Alper Atamturk (University of California, Berkeley) - Sparse Estimation: Closing the Gap Between L0 and L1 Models