Sushant Sachdeva
Sushant Sachdeva
UToronto
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Approximate Gaussian Elimination for Laplacians-fast, sparse, and simple
R Kyng, S Sachdeva
2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016
1312016
Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator
L Orecchia, S Sachdeva, NK Vishnoi
Proceedings of the 44th symposium on Theory of Computing, 1141-1160, 2012
1082012
Sparsified cholesky and multigrid solvers for connection laplacians
R Kyng, YT Lee, R Peng, S Sachdeva, DA Spielman
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
1022016
Provable ICA with unknown Gaussian noise, and implications for Gaussian mixtures and autoencoders
S Arora, R Ge, A Moitra, S Sachdeva
Algorithmica 72 (1), 215-236, 2015
882015
Algorithms for Lipschitz learning on graphs
R Kyng, A Rao, S Sachdeva, DA Spielman
Proceedings of The 28th Conference on Learning Theory, 1190-1223, 2015
632015
Finding overlapping communities in social networks: Toward a rigorous approach
S Arora, R Ge, S Sachdeva, G Schoenebeck
Proceedings of the 13th ACM Conference on Electronic Commerce, 37-54, 2012
612012
Sampling random spanning trees faster than matrix multiplication
D Durfee, R Kyng, J Peebles, AB Rao, S Sachdeva
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017
542017
Faster Algorithms via Approximation Theory
S Sachdeva, NK Vishnoi
Foundations and Trends® in Theoretical Computer Science 9 (2), 125-210, 2014
532014
Convergence Results for Neural Networks via Electrodynamics
R Panigrahy, A Rahimi, S Sachdeva, Q Zhang
9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2017
48*2017
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
R Kyng, A Rao, S Sachdeva
Advances in Neural Information Processing Systems, 2701-2709, 2015
462015
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation via Short Cycle Decompositions
T Chu, Y Gao, R Peng, S Sachdeva, S Sawlani, J Wang
SIAM Journal on Computing, FOCS18-85-FOCS18-157, 2020
372020
Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model
G Zhang, L Li, Z Nado, J Martens, S Sachdeva, G Dahl, C Shallue, ...
Advances in Neural Information Processing Systems, 8196-8207, 2019
352019
Iterative Refinement for p-norm Regression
D Adil, R Kyng, R Peng, S Sachdeva
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019
342019
A framework for analyzing resparsification algorithms
R Kyng, J Pachocki, R Peng, S Sachdeva
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017
292017
Optimal inapproximability for scheduling problems via structural hardness for hypergraph vertex cover
S Sachdeva, R Saket
Computational Complexity (CCC), 2013 IEEE Conference on, 219-229, 2013
262013
Flows in almost linear time via adaptive preconditioning
R Kyng, R Peng, S Sachdeva, D Wang
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019
182019
Faster p-norm minimizing flows, via smoothed q-norm problems
D Adil, S Sachdeva
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
122020
Fast, provably convergent IRLS algorithm for p-norm linear regression
D Adil, R Peng, S Sachdeva
Advances in Neural Information Processing Systems, 14189-14200, 2019
122019
Real analysis in computer science: A collection of open problems
Y Filmus, H Hatami, S Heilman, E Mossel, R O’Donnell, S Sachdeva, ...
Simons Institute, Berkeley, CA, compiled in, 2014
122014
Greedy Geometric Algorithms for Collection of Balls, with Applications to Geometric Approximation and Molecular Coarse‐Graining
F Cazals, T Dreyfus, S Sachdeva, N Shah
Computer Graphics Forum 33 (6), 1-17, 2014
11*2014
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Articles 1–20