Links

Courses

• Convex Optimization:  https://www.stat.cmu.edu/~ryantibs/convexopt/ 

• MIT Deep Learning:  https://udlbook.github.io/udlbook/ 

For lectures

• http://www.hostmath.com

• https://www.desmos.com/3d

• Matrix Visualization (including determinant, eigenvalues/vectors) 

• Likelihood Theory (Univ. Iowa, Graduate level?)

• Moritz Hardt, Convex optimization course at Berkely EECS

• Eugenia Malinnikova, Real Analysis course at Stanford

• High-Dimensional Probability and Statistics

• Math review for new biostatistics students

• [Probability, Mathematical Statistics, and Stochastic Processes](https://www.randomservices.org/)

• Lecture Notes

  • 머신러닝 강의노트 (Andrew Ng) translated by 박수진: https://wikidocs.net/book/587

Theory

• For optimization,

  • https://people.seas.harvard.edu/~yaron/AM221-S16/schedule.html

  • https://ruder.io/optimizing-gradient-descent/

  • 모두를 위한 Convex Optimization: https://convex-optimization-for-all.github.io/#%EC%98%A4%ED%94%88%EC%86%8C%EC%8A%A4-%EC%B0%B8%EC%97%AC%EA%B0%80%EC%9D%B4%EB%93%9C

• For algorithm or overall theory about statistics

  • https://www.secmem.org/blog/page/5/

• Mathematical Theory & Proofs

  • https://sharmaeklavya2.github.io/theoremdep/

  • Orthogonality condition: https://math.stackexchange.com/questions/4100379/condition-for-existence-of-an-orthonormal-matrix-whose-column-space-is-orthogona

• Statistical Theory & Proofs

  • https://myweb.uiowa.edu/pbreheny/7110/wiki/index.html

• Stack Exchanges

  • Reduced-Rank Regression (RRR)

    • https://stats.stackexchange.com/questions/152517/what-is-reduced-rank-regression-all-about

Templates

• Mississippi:   https://brand.msstate.edu/assets/index.php

• Ryan Tibshirani:   https://www.stat.cmu.edu/~ryantibs/convexopt/template.tex

Data Sources

• UK Biobank:  https://www.ukbiobank.ac.uk

Professors

• Jianqing Fan: https://fan.princeton.edu/publications-general.html

• [Homepage of Christian P. Robert](https://www.ceremade.dauphine.fr/~xian/)

• [Lecture Video by Christian P. Robert](https://www.youtube.com/watch?v=BizPtFoyAR4)

• 
  

Programming

• https://www.rdocumentation.org/packages/zeallot/versions/0.1.0

• https://docs.aws.amazon.com/ko_kr/parallelcluster/latest/ug/slurm-mem-based-scheduling-v3.html

• Error "gfortran is not found" in M1 Mac OS:   See https://mac.r-project.org/tools/

• Algorithm Problems (project Euler): https://euler.synap.co.kr/prob_list.php?pg=3

• R vs Rcpp syntax table: https://thecoatlessprofessor.com/programming/cpp/common-operations-with-rcpparmadillo/

• Rcpp PPT: https://privefl.github.io/R-presentation/Rcpp.html?fbclid=IwAR3KGEeqNl4npVrUkiDnDpnHNIPBmkJ5Qp9Q51ysakcLsktCaPNvdr7JeUU#1

• Error:  "Powerpoint Equations do not replace greek letters after hitting space"
  >> Deleting: ~/Library/Group Containers/UBF8T346G9.Office
  https://answers.microsoft.com/en-us/msoffice/forum/all/powerpoint-equations-do-not-replace-greek-letters/8aff6c0b-21b8-45be-b46b-c1a80bca8306

• 
  

Miscellaneous

English To Korean Dictionray

• Google Spreadsheet:  https://docs.google.com/spreadsheets/d/e/2PACX-1vQALBXPaV_9u1Lpgg3n0E30eQKPLZVieqi17scM5upFnXX0Ulm8EvpWzfKgQq5PaFebWTKf9BqAMTsj/pubhtml 

Papers to be read

• Modeling and Learning on High-Dimensional Matrix-Variate Sequences 

• Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

• Unified Transfer Learning in High-Dimensional Linear Regression 

• Comparison of the LASSO and Integrative LASSO with Penalty Factors (IPF-LASSO) methods for multi-omics data: Variable selection with Type I error control 

• Parametric programming-based approximate selective inference for adaptive lasso, adaptive elastic net and group lasso

• Distributed optimal subsampling for quantile regression with massive data 

• Robust transfer learning for high-dimensional regression withlinear constraints

• Generalized ridge regression: a note on negative ridge parameters
  
  Anti-ridge regression for communality estimation in factor analysis

• Selection and Aggregation of Conformal Prediction Sets

• Variable Selection for Generalized Linear Model with Highly Correlated Covariates

• Semi-supervised Fr\'echet Regression

• Supervised Topic Modeling: Optimal Estimation and Statistical Inference

• Screen then select: a strategy for correlated predictors in high-dimensional quantile regression

• Penalized joint models of high-dimensional longitudinal biomarkers and a survival outcome

• An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data

• Compositional variable selection in quantile regression for microbiome data with false discovery rate control

• Covariance Regression with High-Dimensional Predictors

• Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank

• Data-Driven Knowledge Transfer in Batch Q∗ Learning 

• 24.05.06

• Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data

• A Bayesian factor analysis model for high-dimensional microbiome count data

• Eigenvector distributions and optimal shrinkage estimators for large covariance and precision matrices

• Regression for matrix-valued data via Kronecker products factorizatio

• Covariance Regression with High-Dimensional Predictors

• Comparison of the LASSO and Integrative LASSO with Penalty Factors (IPF-LASSO) methods for multi-omics data: Variable selection with Type I error control

• Are synthetic health data ‘personal data’? (& 국내 개보위 개인정보 활용 방향)

• Unified Transfer Learning in High-Dimensional Linear Regression

• 24.5.8

• Multivariate reduced rank regression by signal subspace matching

• Transfer learning under the Cox model with interval‐censored data

• Variable Selection for Generalized Linear Model with Highly Correlated Covariates

• Evaluating a shrinkage estimator for the treatment effect in clinical trials

• 24.5.23

• Lp-Norm for Compositional Data: Exploring the CoDa L1-Norm in Penalised Regression

• fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data

• Sparse Group Penalties for bi-level variable selection

• A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent

• 24.6.12

• Simultaneous clustering and estimation of networks in multiple graphical models

• Low-rank tensor regression for selection of grouped variables

• More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization

• Robust Integrative Analysis via Quantile Regression with Homogeneity and Sparsity

• Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models

• Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions

• 24.7.17

• Implications of Data Topologies for Deep Generative Models

• 24.7.18

• High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data

•