10 papers
Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods
Alireza Kabgani, Felipe Lara, Masoud Ahookhosh
Robust learning aims to maintain model performance under noise, corruption, and distributional shifts, which are prevalent in modern machine learning applications. This work shows…
Quasar-Convex Optimization: Fundamental Properties and High-Order Proximal-Point Methods
Masoud Ahookhosh, Jose M. M. de Brito, Alireza Kabgani +2
We study the optimization of (strongly) quasar-convex functions, a class that arises naturally in many machine learning and data science applications due to its favorable propertie…
Extending Linear Convergence of the Proximal Point Algorithm: The Quasar-Convex Case
José de Brito, Felipe Lara, Di Liu
This work investigates the properties of the proximity operator for quasar-convex functions and establishes the convergence of the proximal point algorithm to a global minimizer wi…
Delayed Feedback in Online Non-Convex Optimization: A Non-Stationary Approach with Applications
Felipe Lara, Cristian Vega
We study non-convex delayed-noise online optimization problems by evaluating dynamic regret in the non-stationary setting when the loss functions are quasar-convex. In particular,…
Discontinuous Strongly Quasiconvex Functions
Nguyen Thi Van Hang, Felipe Lara, Nguyen Dong Yen
A fundamental open question asking whether all real-valued strongly quasiconvex functions defined on are necessarily continuous, akin to their convex counterparts, is…
Characterizations of Strongly Quasiconvex Functions
Nicolas Hadjisavvas, Felipe Lara
We provide new necessary and sufficient conditons for ensuring strong quasiconvexity in the nonsmooth case and, as a consequence, we provide a proof for the differentiable case. Fu…