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S. Sankaranarayanan

40 papers hereh-index 448.7k citations251 works total

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

author position
  • sole author1
  • first author1
  • middle author10
  • last author28

Across the 40 of 40 papers where every author was matched, so the position is known.

fields
  • math.OC13
  • eess.SY9
  • cs.RO5
  • cs.LG4
  • cs.GT2
  • cs.PL2
same name
  • S. Sankaranarayanan — 36 papers, h 51
  • S. Sankaranarayanan — 16 papers, h 19
  • S. Sankaranarayanan — 6 papers, h 8
  • S. Sankaranarayanan — 1 paper, h 1
  • S. Sankaranarayanan — 1 paper, h 2
  • S. Sankaranarayanan — 1 paper, h 13

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
20122024
most citedOutput Range Analysis for Deep Neural Networks

75 citations · 261 across the 31 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

math.OC2019★ 1 cited

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

Yoshua Bengio, Emma Frejinger, Andrea Lodi +2

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the f…

cs.GT2019★ 11 cited

When Nash Meets Stackelberg

Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo +2

This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous non-cooperative games where the players solve sequential Stac…

cs.CR2019

Efficient Detection and Quantification of Timing Leaks with Neural Networks

Saeid Tizpaz-Niari, Pavol Cerny, Sriram Sankaranarayanan +1

Detection and quantification of information leaks through timing side channels are important to guarantee confidentiality. Although static analysis remains the prevalent approach f…

eess.SY2019

Formal Policy Learning from Demonstrations for Reachability Properties

Hadi Ravanbakhsh, Sriram Sankaranarayanan, Sanjit A. Seshia

We consider the problem of learning structured, closed-loop policies (feedback laws) from demonstrations in order to control under-actuated robotic systems, so that formal behavior…

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