15 citations · 24 across the 5 of their papers we have counts for
6 papers
Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
Parvin Nazari, Bojian Hou, Davoud Ataee Tarzanagh +2
Online bilevel optimization (OBO) is a powerful framework for machine learning problems where both outer and inner objectives evolve over time, requiring dynamic updates. Current O…
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming
Parvin Nazari, Ahmad Mousavi, Davoud Ataee Tarzanagh +1
Bilevel programming has recently received attention in the literature due to its wide range of applications, including reinforcement learning and hyper-parameter optimization. Howe…
Dynamic Regret of Adaptive Gradient Methods for Strongly Convex Problems
Parvin Nazari, Esmaile Khorram
Adaptive gradient algorithms such as ADAGRAD and its variants have gained popularity in the training of deep neural networks. While many works as for adaptive methods have focused…
Dynamic Regret Analysis for Online Meta-Learning
Parvin Nazari, Esmaile Khorram
The online meta-learning framework has arisen as a powerful tool for the continual lifelong learning setting. The goal for an agent is to quickly learn new tasks by drawing on prio…
Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems
Parvin Nazari, Davoud Ataee Tarzanagh, George Michailidis
In this paper, we design and analyze a new family of adaptive subgradient methods for solving an important class of weakly convex (possibly nonsmooth) stochastic optimization probl…
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
Parvin Nazari, Davoud Ataee Tarzanagh, George Michailidis
Adaptive gradient-based optimization methods such as \textsc{Adagrad}, \textsc{Rmsprop}, and \textsc{Adam} are widely used in solving large-scale machine learning problems includin…