7 papers · 1 filter
Tensor Envelopes in the Nonconvex Setting
Alireza Kabgani, Masoud Ahookhosh
We introduce a tensor envelope (TEE) for composite objectives , built from a th-order Taylor model of the smooth term regularized by a th power with . To cons…
Beyond Conventional Federated Learning via High-Order Regularization
Alireza Kabgani, Masoud Ahookhosh
Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regularization of FedProx grows l…
Difference-of-Convex Optimization via Inexact Smoothing Descent Methods: Difference of High-Order Moreau Envelopes
Alireza Kabgani, Moslem Zamani, Masoud Ahookhosh
This paper studies difference-of-convex (DC) optimization problems through smoothing descent techniques. In particular, we introduce the difference of high-order Moreau envelopes (…
Relative Weak Convexity and Projected Subgradient Methods: Analysis and Convergence
Morteza Rahimi, Masoud Ahookhosh
We introduce the class of relatively weakly convex functions, which extends the classical notion of weak convexity by measuring nonconvexity relative to a distance-generating funct…
Speeding Up Nonsmooth Bayesian MCMC Sampling via Inexact Proximal Unadjusted Langevin Algorithm
Susan Ghaderi, Alireza Kabgani, Yves Moreau +1
We study sampling from posterior distributions with nonsmooth composite potentials, a setting in which proximal-based Langevin methods are theoretically appealing but in practice l…
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…