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researcher

Naman Agarwal

Google Research

39 papers hereh-index 212.7k citations54 works total

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

author position
  • first author27
  • middle author11

Across the 38 of 39 papers where every author was matched, so the position is known.

fields
  • cs.LG27
  • math.OC3
  • stat.ML3
  • cs.CC1
  • cs.CR1
  • cs.CV1
affiliations
  • Google Research
Homepage
same name
  • Naman Agarwal — 6 papers, h 3
  • Naman Agarwal — 5 papers, h 8
  • Naman Agarwal — 2 papers, h 1
  • Naman Agarwal — 2 papers, h 1
  • Naman Agarwal — 1 paper
  • Naman Agarwal — 1 paper, h 0

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
20132024
most citedcpSGD: Communication-efficient and differentially-private distributed SGD

184 citations · 526 across the 38 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 51 cited

Logarithmic Regret for Online Control

Naman Agarwal, Elad Hazan, Karan Singh

We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics. This…

cs.LG2019★ 5 cited

Boosting for Control of Dynamical Systems

Naman Agarwal, Nataly Brukhim, Elad Hazan +1

We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online contro…

cs.LG2019★ 54 cited

Online Control with Adversarial Disturbances

Naman Agarwal, Brian Bullins, Elad Hazan +2

We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online c…

cs.LG2019

Extreme Tensoring for Low-Memory Preconditioning

Xinyi Chen, Naman Agarwal, Elad Hazan +2

State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient…

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