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M. Mahdavi

43 papers hereh-index 347.7k citations92 works total

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

author position
  • sole author1
  • first author8
  • middle author15
  • last author19

Across the 43 of 43 papers where every author was matched, so the position is known.

fields
  • cs.LG40
  • math.OC2
  • cs.SI1
same name
  • M. Mahdavi — 5 papers, h 3
  • M. Mahdavi — 2 papers, h 13
  • M. Mahdavi — 2 papers, h 13
  • M. Mahdavi — 2 papers, h 3
  • M. Mahdavi — 2 papers, h 10
  • M. Mahdavi — 1 paper, h 46

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
20112026
most citedAdaptive Personalized Federated Learning

339 citations · 1k across the 41 of their papers we have counts for

collaborators
Showing 2014Show all

4 papers · 1 filter

cs.SI2014★ 4 cited

Matrix Factorization with Explicit Trust and Distrust Relationships

Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard +1

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring…

cs.LG2014★ 3 cited

Exploiting Smoothness in Statistical Learning, Sequential Prediction, and Stochastic Optimization

Mehrdad Mahdavi

In the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algori…

cs.LG2014★ 4 cited

Excess Risk Bounds for Exponentially Concave Losses

Mehrdad Mahdavi, Rong Jin

The overarching goal of this paper is to derive excess risk bounds for learning from exp-concave loss functions in passive and sequential learning settings. Exp-concave loss functi…

cs.LG2014★ 4 cited

Binary Excess Risk for Smooth Convex Surrogates

Mehrdad Mahdavi, Lijun Zhang, Rong Jin

In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of mor…

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