Evidence map›Paper›PMID 28316509›Full record

ArticleJournal of machine learning research : JMLR2015

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

Han Liu, Lie Wang, Tuo Zhao

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Journal of machine learning research : JMLR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Han LiuDepartment of Operations Research and Financial Engineering, Princeton University, NJ 08544, USA, hanliu@princeton.edu.
Lie WangDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge MA 02139, USA, liewang@math.mit.edu.
Tuo Zhao

Funding

Preprocessing and Analysis Tools for High-Throughput TechnologiesR01GM083084 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2007 to 2018
$4.4M
Large-Scale Semiparametric Graphical Models with Applications to NeuroscienceR01MH102339 · NIMH · PRINCETON UNIVERSITY · PI LIU, HAN · 2014 to 2017
$1.2M
Statistical and Computational Tools for Next-generation ChIP-seq ApplicationsR01HG006841 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI JI, HONGKAI · 2012 to 2014
$951k
NHGRI NIH HHS R01 HG006841NIGMS NIH HHS R01 GM083084NIMH NIH HHS R01 MH102339
6 · The paper itself

Abstract

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence

Indexed as

brain activity predictioncalibrationhigh dimensionlow Rankmultivariate regressionsparsity

Identifiers

PMID28316509
PMCPMC5354374

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.