Large Scale Regression With Sparse Data

By Editorial Team · September 27, 2026 · 5 min read

Large Scale Regression With Sparse Data Information Guide

  1. Background to Large Scale Regression With Sparse Data
  2. Main Features
  3. Developments
  4. Expert Insights
  5. Final Thoughts

Background to Large Scale Regression With Sparse Data

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Main Features

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Developments

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Hui Zou: Sparse Convoluted Rank Regression in High Dimensions
Hui Zou: Sparse Convoluted Rank Regression in High Dimensions
Min-Wise Hashing for Large-Scale Regression and Classification
Min-Wise Hashing for Large-Scale Regression and Classification
Robust, Interpretable Statistical Models: Sparse Regression with the LASSO
Robust, Interpretable Statistical Models: Sparse Regression with the LASSO
Marc Ratkovic, Sparse Estimation and Uncertainty with Application to Subgroup Analysis
Marc Ratkovic, Sparse Estimation and Uncertainty with Application to Subgroup Analysis
Hussein Hazimeh - Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
Hussein Hazimeh - Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
Joseph Salmon (ENST-ParisTech): GAP Safe Rules to Speed up Sparse Multitask Regression
Joseph Salmon (ENST-ParisTech): GAP Safe Rules to Speed up Sparse Multitask Regression
OHBM 2019 | Oral Sessions | Multi - Scale Network Regression: A new low - rank and sparse mult…
OHBM 2019 | Oral Sessions | Multi - Scale Network Regression: A new low - rank and sparse mult…
Sparse Regression Comparison
Sparse Regression Comparison
Sparse Identification and Estimation of Large-Scale Vector Autoregressive Moving Averages
Sparse Identification and Estimation of Large-Scale Vector Autoregressive Moving Averages
On the Power of Preconditioning in Sparse Linear Regression
On the Power of Preconditioning in Sparse Linear Regression
Sparse and large-scale learning with heterogeneous data
Sparse and large-scale learning with heterogeneous data

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Last Updated: September 27, 2026

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Summary

Constantine Caramanis (University of Texas at Austin) ... From 25 June to 14 September 2018, 20 interns worked across nine projects. This series is a compilation of their final ... American Statistical Association (ASA), Section on Statistical Learning and Nicolai Meinshausen, University of Oxford and ETH Zürich Succinct Marc Ratkovic of Princeton University presented a talk entitled " Part of Discrete Optimization Talks: talks.discreteopt.com Hussein Hazimeh -- MIT Talk given by Joseph Salmon at CIMAT on November, 6th, during the Workshop on Image Processing/Statistical Pattern ... OHBM 2019 Oral Sessions Title: Multi - A comparison between the results of Speaker: David Matteson (Cornell) Guest Panellists: Yao Zheng (Connecticut) and Ines Wilms (Maastricht) Raghu Meka (UCLA) simons.berkeley.edu/talks/power-preconditioning- Google Tech Talks September 5, 2006 Gert Lanckriet is assistant professor in the Electrical and Computer Engineering ...

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