2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.9.3 | ||
| Paper Title | On the alpha-loss Landscape in the Logistic Model | ||
| Authors | Tyler Sypherd, Arizona State University, United States; Mario Diaz, Universidad Nacional Autónoma de México, Mexico; Lalitha Sankar, Gautam Dasarathy, Arizona State University, United States | ||
| Session | L.9: Learning Methods and Networks | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We analyze the optimization landscape of a recently introduced tunable class of loss functions called alpha-loss, alpha in (0,infinity], in the logistic model. This family encapsulates the exponential loss (alpha = 1/2), the log-loss (alpha = 1), and the 0-1 loss (alpha = infinity) and contains compelling properties that enable the practitioner to discern among a host of operating conditions relevant to emerging learning methods. Specifically, we study the evolution of the optimization landscape of alpha-loss with respect to alpha using tools drawn from the study of strictly-locally-quasi-convex functions in addition to geometric techniques. We interpret these results in terms of optimization complexity via normalized gradient descent. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
