Learning Some Popular Gaussian Graphical Models without Condition Number
Bounds

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Learning Some Popular Gaussian Graphical Models without Condition Number Bounds

3 May 2019

Jonathan A. Kelner

Frederic Koehler

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Papers citing "Learning Some Popular Gaussian Graphical Models without Condition Number Bounds"

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Title
Efficient Hamiltonian, structure and trace distance learning of Gaussian states Marco Fanizza Cambyse Rouzé Daniel Stilck França 85 6 0 05 Nov 2024
A New Approach to Learning Linear Dynamical Systems Ainesh Bakshi Allen Liu Ankur Moitra Morris Yau 83 16 0 23 Jan 2023
Statistical Efficiency of Score Matching: The View from Isoperimetry Frederic Koehler Alexander Heckett Andrej Risteski DiffM 153 52 0 03 Oct 2022
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs Jonathan A. Kelner Frederic Koehler Raghu Meka Dhruv Rohatgi 34 2 0 05 Mar 2022
Learning with latent group sparsity via heat flow dynamics on networks Subhro Ghosh Soumendu Sundar Mukherjee AI4CE 57 2 0 20 Jan 2022
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models Enric Boix-Adserà Guy Bresler Frederic Koehler TPM 68 10 0 07 Jun 2021
Sample-Optimal and Efficient Learning of Tree Ising models C. Daskalakis Qinxuan Pan 64 8 0 28 Oct 2020
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent Matthew Brennan Guy Bresler Samuel B. Hopkins Jingkai Li T. Schramm 84 66 0 13 Sep 2020