Search Results for author:
Found 7 papers, 1 papers with code
$\ell_p$-Regression in the Arbitrary Partition Model of Communication
We consider the randomized communication complexity of the distributed $\ell_p$-regression problem in the coordinator model, for $p\in (0, 2]$.
Learning the Positions in CountSketch
We fix this issue and propose approaches for learning a sketching matrix for both low-rank approximation and Hessian approximation for second order optimization.
Triangle and Four Cycle Counting with Predictions in Graph Streams
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 the graph data stream literature.
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
We achieve this by establishing a direct connection between robust learning of Bayesian networks and robust mean estimation.
Learning-Augmented Sketches for Hessians
We show how to design learned sketches for the Hessian in the context of second order methods.
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues.
Learning-Augmented Data Stream Algorithms
For estimating the $p$-th frequency moment for $0 < p < 2$ we obtain the first algorithms with optimal update time.
