Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares

17 December 2019

Papers citing "Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares"

4 / 4 papers shown

Title
Importance Sampling for Nonlinear Models Prakash Palanivelu Rajmohan Fred Roosta 4 0 0 18 May 2025
Causal Deep Learning M. Alex O. Vasilescu CML 65 2 1 03 Jan 2025
$On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of $\mathbb{R}^N$$ On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of $\mathbb{R}^N$ M. Iwen M. Roach 8 1 0 07 Jun 2022
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees Osman Asif Malik 36 14 0 14 Oct 2021