Gene regulatory network inference using sparse probabilistic models Evelina Gabaˇsov´a Supervisor: David Barber

Source URL: s3-eu-west-1.amazonaws.com

• Statistics
• Econometrics
• Estimation theory
• Networks
• Statistical methods
• Regression analysis
• Linear regression
• Gene regulatory network
• Bayesian network
• Least squares
• Machine learning
• Lasso

Model-based Approaches to Robust Speech Recognition in Diverse Environments Yongqiang Wang Darwin College Engineering Department Cambridge University

Source URL: mi.eng.cam.ac.uk

• Estimation theory
• Signal processing
• Computer accessibility
• Speech recognition
• Linear regression
• Maximum likelihood estimation

Alternating Minimization for Regression Problems with Vector-valued Outputs Prateek Jain Microsoft Research, INDIA

Source URL: dept.stat.lsa.umich.edu

• Regression analysis
• Estimation theory
• Covariance and correlation
• Parametric statistics
• Statistical theory
• Linear regression
• Estimation of covariance matrices
• Ordinary least squares
• Normal distribution
• Maximum likelihood estimation
• Covariance
• Errors and residuals

arXiv:1405.3564v1 [cond-mat.mtrl-sci] 14 MayAn Investigation of Machine Learning Methods Applied to Structure Prediction in Condensed Matter William J. Brouwer1,a James D. Kubicki,b Jorge O. Sofo,c C. Lee Gilesd a

Source URL: clgiles.ist.psu.edu

• Nuclear magnetic resonance
• Econometrics
• Spectroscopy
• Solid-state nuclear magnetic resonance
• Regression analysis
• Linear regression
• Nuclear magnetic resonance crystallography
• Residual dipolar coupling

JMLR: Workshop and Conference Proceedings vol 40:1–14, 2015 Minimax Fixed-Design Linear Regression Peter L. Bartlett BARTLETT @ CS . BERKELEY. EDU

Source URL: jmlr.org

• Mathematics
• Estimation theory
• Regression analysis
• Statistics
• Decision theory
• Game theory
• Minimax
• Linear regression
• Regret
• Statistical theory

Ann Inst Stat Math:329–351 DOIs10463Expectation-robust algorithm and estimating equations for means and dispersion matrix with missing data Ke-Hai Yuan · Wai Chan · Yubin Tian

Source URL: www.ism.ac.jp

• Statistics
• Estimation theory
• Covariance matrix
• Estimator
• Maximum likelihood estimation
• Variance
• Covariance
• Regression analysis
• M-estimator
• Normal distribution
• Linear regression
• Expected value

Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is now modeled as a f

Source URL: www.stat.columbia.edu

On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain∗ Ambuj Tewari†

Source URL: dept.stat.lsa.umich.edu

• Statistics
• Statistical theory
• Estimation theory
• Sparse approximation
• Bias of an estimator
• Consistent estimator
• Loss function
• M-estimator
• Linear regression
• Sufficient statistic

Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling Peter Hooper Yasin Abbasi-Yadkori, Russ Greiner, Bret Hoehn Dept of Mathematical & Statistical Sciences

Source URL: webdocs.cs.ualberta.ca

• Probability distributions
• Estimation theory
• Econometrics
• Statistical theory
• Summary statistics
• Normal distribution
• Bayesian network
• Beta distribution
• Mode
• Linear regression
• Delta method
• Gamma distribution

ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models Sanjay Krishnan, Jiannan Wang, Eugene Wu † , Michael J. Franklin, Ken Goldberg UC Berkeley, † Columbia University {sanjaykrishnan, jnwang, fran

Source URL: sampleclean.org

• Statistics
• Statistical theory
• Estimator
• Statistical inference
• M-estimators
• Convex optimization
• Stochastic gradient descent
• Bias of an estimator
• Loss function
• Data analysis
• Linear regression
• Generalised Hough transform