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Aryan Mokhtari

University of Texas at Austin

83 papers hereh-index 409.5k citations129 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author23
  • middle author49
  • last author10

Across the 82 of 83 papers where every author was matched, so the position is known.

fields
  • math.OC42
  • cs.LG36
  • stat.ML2
  • cs.DC1
  • cs.IT1
  • cs.MA1
affiliations
  • University of Texas at Austin
Homepage
same name
  • Aryan Mokhtari — 16 papers, h 6
  • Aryan Mokhtari — 7 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedPersonalized Federated Learning: A Meta-Learning Approach

362 citations · 2.5k across the 76 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

math.OC2021

Minimax Optimization: The Case of Convex-Submodular

Arman Adibi, Aryan Mokhtari, Hamed Hassani

Minimax optimization has been central in addressing various applications in machine learning, game theory, and control theory. Prior literature has thus far mainly focused on study…

math.OC2021

Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach

Qiujiang Jin, Aryan Mokhtari

In this paper, we study the application of quasi-Newton methods for solving empirical risk minimization (ERM) problems defined over a large dataset. Traditional deterministic and s…

cs.LG2021★ 69 cited

Exploiting Shared Representations for Personalized Federated Learning

Liam Collins, Hamed Hassani, Aryan Mokhtari +1

Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. Ho…

cs.LG2021★ 4 cited

Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks

Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar

In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems. We focus on the setting in which we train…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.