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Shivam Garg

10 papers hereh-index 7959 citations16 works total

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

author position
  • first author6
  • middle author4

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.DS3
  • cs.CL1
  • cs.NE1
  • math.ST1
same name
  • Shivam Garg — 9 papers, h 4
  • Shivam Garg — 6 papers, h 5
  • Shivam Garg — 1 paper
  • Shivam Garg — 1 paper, h 1
  • Shivam Garg — 1 paper, 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
20152024
most citedWhat Can Transformers Learn In-Context? A Case Study of Simple Function Classes

59 citations · 88 across the 9 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

Discovering Data Structures: Nearest Neighbor Search and Beyond

Omar Salemohamed, Laurent Charlin, Shivam Garg +2

We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and…

cs.LG2020★ 9 cited

Gradient Temporal-Difference Learning with Regularized Corrections

Sina Ghiassian, Andrew Patterson, Shivam Garg +3

It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems…

cs.LG2019★ 2 cited

Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Brian Axelrod, Shivam Garg, Vatsal Sharan +1

Given data drawn from an unknown distribution, D, to what extent is it possible to ``amplify'' this dataset and output an even larger set of samples that appear to have been draw…

cs.LG2018★ 14 cited

A Spectral View of Adversarially Robust Features

Shivam Garg, Vatsal Sharan, Brian Hu Zhang +1

Given the apparent difficulty of learning models that are robust to adversarial perturbations, we propose tackling the simpler problem of developing adversarially robust features.…

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