activity
20182022
most citedLearning-based Support Estimation in Sublinear Time

8 citations · 18 across the 9 of their papers we have counts for

collaborators

19 papers

cs.DS2022

Estimating the Effective Support Size in Constant Query Complexity

Shyam Narayanan, Jakub Tětek

Estimating the support size of a distribution is a well-studied problem in statistics. Motivated by the fact that this problem is highly non-robust (as small perturbations in the d…

cs.DS2022

Sampling an Edge in Sublinear Time Exactly and Optimally

Talya Eden, Shyam Narayanan, Jakub Tětek

Sampling edges from a graph in sublinear time is a fundamental problem and a powerful subroutine for designing sublinear-time algorithms. Suppose we have access to the vertices of…

cs.DS20226 cited

Improved Approximations for Euclidean -means and -median, via Nested Quasi-Independent Sets

Vincent Cohen-Addad, Hossein Esfandiari, Vahab Mirrokni +1

Motivated by data analysis and machine learning applications, we consider the popular high-dimensional Euclidean -median and -means problems. We propose a new primal-dual alg…

cs.DS20222 cited

All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights

Justin Y. Chen, Shyam Narayanan, Yinzhan Xu

We revisit the problem of privately releasing the all-pairs shortest path distances of a weighted undirected graph up to low additive error, which was first studied by Sealfon [Sea…

cs.DS20221 cited

Triangle and Four Cycle Counting with Predictions in Graph Streams

Justin Y. Chen, Talya Eden, Piotr Indyk +7

We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in…

cs.ET20221 cited

Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems

Matteo Cartiglia, Arianna Rubino, Shyam Narayanan +4

The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip on…