Paper Stack for Ke Yu

[kayhan-batmanghelich]

• Cooper GF. A simple algorithm for efficiently mining observational databases for causal relationships
• Detection of pharmacovigilance-related adverse events using electronic health records and automated methods
• Benazepril plus Amlodipine or Hydrochlorothiazide for Hypertension in High-Risk Patients
• Effects of Losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy
• Bringing cohort studies to the bedside: framework for a ‘green button’ to support clinical decision-making
• Evidence-Based Medicine in the EMR Era
• Wang, S., Nan, B., Rosset, S., & Zhu, J. (2011). Random lasso. The annals of applied statistics, 5(1), 468.
• Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.
• Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P., & Sugiyama, M. (2014). High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1), 185-207.
• Gretton, A., Bousquet, O., Smola, A., & Scholkopf, B. (2005, October). Measuring statistical dependence with Hilbert-Schmidt norms. In ALT (Vol. 16, pp. 63-78).
• Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.
• Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.
• Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature biotechnology, 31(8), 726-733.
• Lengerich BJ, Aragam B, Xing EP. Personalized regression enables sample-specific pan-cancer analysis. Bioinformatics, Volume 34, Issue 13, 1 July 2018
• Eric V. Strobl, Kun Zhang, Shyam Visweswaran Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
• Yang Ni, Peter Muller, Yuan Ji Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records
• Sinno Jialin Pan, Qiang Yang A Survey on Transfer Learning
• Zhang, Jinghe, Kowsari, Kamran, and et. al., Patient2Vec: Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

[gatechke]

Added paper 3 - 6 that were assigned by Shyam.

[kayhan-batmanghelich]

@gatechke add the following papers to your paper stack:

[1] Wang, S., Nan, B., Rosset, S., & Zhu, J. (2011). Random lasso. The annals of applied statistics, 5(1), 468.
[2] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.
[3] Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P., & Sugiyama, M. (2014). High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1), 185-207.

[gatechke]

Paper 8 Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.

Read but need revision after filling knowledge gaps of a couple of building blocks - probabilistic graphical modal, convex optimization

[kayhan-batmanghelich]

@gatechke some of those need a formal course but you should not postpone learning about these topics until you take the course. Find tutorials about convex optimization and PGM and post it here to your paper stack. Both PGM and CVXOPT are huge topics, so read parts of the tutorial that are related to the paper. You have already been in research, you should be able to find right resources but if you couldn't let me know, I will post a few.

[kayhan-batmanghelich]

Add these to your paper stack:

Gretton, A., Bousquet, O., Smola, A., & Scholkopf, B. (2005, October). Measuring statistical dependence with Hilbert-Schmidt norms. In ALT (Vol. 16, pp. 63-78).

Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.

Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.

Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature biotechnology, 31(8), 726-733.

@gatechke in a few weeks I am going to ask you to summarize three papers in a presentation, so read them carefully.

[shyamvis]

Add this to paper stack of methods papers:

Lengerich BJ, Aragam B, Xing EP. Personalized regression enables sample-specific pan-cancer analysis. Bioinformatics, Volume 34, Issue 13, 1 July 2018

[gatechke]

@kayhan-batmanghelich @shyamvis
Thank you for posting the papers. I found some tutorials to help me in these subjects. Please let me know if you have any suggestion.

Probabilistic Graphical Modal

• CMU lectures: Probabilistic Graphical Models(https://kayhan.dbmi.pitt.edu/node/38)
• Coursera: PGM Specialization (Daphne Koller) (https://www.coursera.org/specializations/probabilistic-graphical-models)
• Textbook: Michael Jordan. Introduction to Graphical Models. in preparation

Convex Optimization

• CMU lectures: Convex Optimization (http://www.cs.cmu.edu/~pradeepr/convexopt/)
• Stanford lectures: https://www.youtube.com/watch?v=McLq1hEq3UY&list=PL3940DD956CDF0622

Causal Inference

• NYU lecture: Causal Inference for Observational Studies (https://cs.nyu.edu/~shalit/slides.pdf)

Functional Analysis

• MIT lecture: Introduction to Functional Analysis (https://ocw.mit.edu/courses/mathematics/18-102-introduction-to-functional-analysis-spring-2009/index.htm)
• Coursera: Introduction to Functional Analysis (https://www.youtube.com/playlist?list=PLSpInro6Ys2IHve6oN9h005zmwfLnOSp1)

Multivariate Statistical Analysis

• Textbook: Wolfgang H¨ardle, Applied Multivariate Statistical Analysis