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researcher

S. Kulkarni

34 papers hereh-index 427.3k citations240 works total

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

author position
  • middle author22
  • last author9

Across the 31 of 34 papers where every author was matched, so the position is known.

fields
  • cs.IT16
  • cs.IR3
  • cs.AI2
  • cs.DC2
  • math.PR2
  • stat.ME2
same name
  • S. Kulkarni — 320 papers, h 104
  • S. Kulkarni — 113 papers, h 58
  • S. Kulkarni — 89 papers, h 28
  • S. Kulkarni — 60 papers, h 32
  • S. Kulkarni — 35 papers, h 32
  • S. Kulkarni — 22 papers, h 5

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
20052021
most citedMachine Learning Methods for Attack Detection in the Smart Grid

655 citations · 1.6k across the 31 of their papers we have counts for

collaborators
Showing 2007Show all

4 papers · 1 filter

stat.ML2007★ 157 cited

Probabilistic coherence and proper scoring rules

Joel Predd, Robert Seiringer, Elliott H. Lieb +3

We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The t…

math.PR2007

Density estimation from an individual numerical sequence

Andrew B. Nobel, Gusztav Morvai, Sanjeev R. Kulkarni

This paper considers estimation of a univariate density from an individual numerical sequence. It is assumed that (i) the limiting relative frequencies of the numerical sequence ar…

math.PR2007

Regression estimation from an individual stable sequence

Gusztav Morvai, Sanjeev R. Kulkarni, Andrew B. Nobel

We consider univariate regression estimation from an individual (non-random) sequence (x1​,y1​),(x2​,y2​),...∈ℜ×ℜ, which is stable in the sense that for each…

cs.IT2007

A Better Good-Turing Estimator for Sequence Probabilities

Aaron B. Wagner, Pramod Viswanath, Sanjeev R. Kulkarni

We consider the problem of estimating the probability of an observed string drawn i.i.d. from an unknown distribution. The key feature of our study is that the length of the observ…

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