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researcher

S. Keerthi

24 papers hereh-index 3813.6k citations121 works total

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

author position
  • first author1
  • middle author12
  • last author10

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • cs.LG19
  • stat.ML2
  • cs.AI1
  • cs.CV1
  • cs.DB1
same name
  • S. Keerthi — 6 papers, h 6
  • S. Keerthi — 1 paper, h 3
  • S. Keerthi — 1 paper, h 6
  • S. Keerthi — 1 paper, h 9

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
20112023
most citedGradient Boosting Neural Networks: GrowNet

55 citations · 152 across the 23 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 3 cited

Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space

Khoa D. Doan, Saurav Manchanda, Fengjiao Wang +3

For a given image generation problem, the intrinsic image manifold is often low dimensional. We use the intuition that it is much better to train the GAN generator by minimizing th…

cs.LG2020★ 2 cited

Regression via Implicit Models and Optimal Transport Cost Minimization

Saurav Manchanda, Khoa Doan, Pranjul Yadav +1

This paper addresses the classic problem of regression, which involves the inductive learning of a map, y=f(x,z), z denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarr…

cs.LG2020

Targeted display advertising: the case of preferential attachment

Saurav Manchanda, Pranjul Yadav, Khoa Doan +1

An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital adve…

cs.LG2020★ 55 cited

Gradient Boosting Neural Networks: GrowNet

Sarkhan Badirli, Xuanqing Liu, Zhengming Xing +3

A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework…

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