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Aarti Singh

12 papers hereh-index 141.1k citations19 works total

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

author position
  • middle author8
  • last author4

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • stat.ML8
  • math.ST3
  • math.AT1
same name
  • Aarti Singh — 19 papers, h 18
  • Aarti Singh — 11 papers, h 8
  • Aarti Singh — 8 papers, h 8
  • Aarti Singh — 7 papers, h 7
  • Aarti Singh — 7 papers, h 5
  • Aarti Singh — 6 papers, h 7

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
20102013
most citedConfidence sets for persistence diagrams

254 citations · 259 across the 4 of their papers we have counts for

collaborators
Showing 2013 · stat.MLShow all

4 papers · 2 filters

stat.ML2013

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic

James Sharpnack, Akshay Krishnamurthy, Aarti Singh

The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity moni…

stat.ML2013★ 3 cited

Tight Lower Bounds for Homology Inference

Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh +1

The homology groups of a manifold are important topological invariants that provide an algebraic summary of the manifold. These groups contain rich topological information, for ins…

stat.ML2013

Cluster Trees on Manifolds

Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo +2

In this paper we investigate the problem of estimating the cluster tree for a density f supported on or near a smooth d-dimensional manifold M isometrically embedded in $\mat…

stat.ML2013

Distribution-Free Distribution Regression

Barnabas Poczos, Alessandro Rinaldo, Aarti Singh +1

`Distribution regression' refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y=f(P) + mu where f is an unknown…

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