Useful for referring—12/12/2020

1. On writing well: Through the Eyes of Birds and Frogs : This article is a very interesting and helpful article about writing especially for academic papers and review articles.
   • “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon. They delight in concepts that unify our thinking. “
   • “Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects.”
   • “That the aim of our writing, whatever we might be writing, is to familiarise the strange and to mystify the familiar. “
   • “Start writing with one simple fact that readers must know. Then add more by broadening this first point. The third adds to the second and broadens the reader’s set of facts and connections. Finally we achieve a new state of wisdom.”
2. On On Offline Reinforcement Learning: @NandoDF: Offline Reinforcement Learning frees us to think about bigger problems than we have before. @JohnCLangford: Offline reinforcement learning might be the key to reinforcement learning.
   • NIPS 2020 Offline Reinforcement Learning Workshop
   • Statisticians need to learn such kind of reward driven framework using offline observed data. It’s a big change which makes us think more.
   • Reinforcement Learning from Batch Data and Simulation
3. NIPS 2020 Interesting Talks:
   • Causal Learning
   • Workshop: Causal Discovery and Causality-Inspired Machine Learning
   • Workshop: AI for Earth Sciences
   • Workshop: Self-Supervised Learning — Theory and Practice
   • Tutorial: Offline Reinforcement Learning: From Algorithm Design to Practical Applications

Useful for referring—01-16-2019

1. A nice blog on CS including learnings: https://blog.acolyer.org/ called “the morning paper”: an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer. I hope there is a similar blog on Statistics, reviewing and recommending an interesting/influential/important paper from the world of Statistics.
2. A wonderful summary of Mathematical Tricks Commonly Used in Machine Learning and Statistics with examples
3. I just realized that when I teach ridge regression I should have used A Useful Matrix Inverse Equality for Ridge Regression
4. GANs should be gained much attention in the stats community: Understanding Generative Adversarial Networks. This is a nice post about GANs based on “probably the highest-quality general overview available nowadays: Ian Goodfellow’s tutorial on arXiv, which he then presented in some form at NIPS 2016. “
5. R or Python? Why not both? Using Anaconda Python within R with {reticulate}
6. “A heatmap is basically a table that has colors in place of numbers. Colors correspond to the level of the measurement.”

Use StatRep for Integrating SAS in Beamer Slides

You can install the StatRep package by downloading statrep.zip from support.sas.com/StatRepPackage, which contains:

• doc/statrepmanual.pdf – The StatRep User’s Guide (this manual)
• doc/quickstart.tex – A template and tutorial sample LATEX file
• sas/statrep_macros.sas – The StatRep SAS macros
• sas/statrep_tagset.sas – The StatRep SAS tagset for LaTeX tabular output
• statrep.ins – The LATEX package installer file
• statrep.dtx – The LATEX package itself

Unzip the file statrep.zip to a temporary directory and perform the following steps:

• Step 1: Install the StatRep SAS Macros: Copy the file statrep_macros.sas to a local directory. If you have a folder where you keep your personal set of macros, copy the file there. Otherwise, create a directory such as C:\mymacros and copy the file into that directory.
• Step 2: Install the StatRep LaTeX Package: These instructions show how to install the StatRep package in your LATEX distribution for your personal use.
  • a. For MikTEX users: If you do not have a directory for your own packages, choose a directory name to contain your packages (for example, C:\localtexmf). In the following instructions, this directory is referred to as the “root directory”.
  • b. Create the additional subdirectories under the above root directory: tex/latex/statrep. Your directory tree will have the following structure: root directory/tex/latex/statrep.
  • c. Copy the files statrep.dtx, statrep.ins, statrepmanual.pdf, and statrepmanual.tex to the statrep subdirectory.
  • d. In the command prompt, cd to the statrep directory and enter the following command: pdftex statrep.insThe command creates several files, one of which is the configuration file, statrep.cfg.
• Step 3: Tell the StatRep Package the Location of the StatRep SAS Macros. Edit the statrep.cfg file that was generated in Step 2d so that the macro \SRmacropath contains the correct location of the macro file from step 1. For example, if you copied the statrep_macros.sas file to a directory named C:\mymacros, then you de- fine macro \SRmacropath as follows: \def\SRmacropath{C:/mymacros/statrep_macros.sas} Use the forward slash as the directory name delimiter instead of the backslash, which is a special character in LaTeX.

You can now test and experiment with the package. Create a working directory, and copy the file quickstart.tex into it. To generate the quick-start document:

1. Compile the document with pdfLATEX. You can use a LATEX-aware editor such as TEXworks, or use the command-line command pdflatex. This step generates the SAS program that is needed to produce the results.
2. Execute the SAS program quickstart_SR.sas, which was automatically created in the preceding step. This step generates the SAS results that are requested in the quick-start document.
3. Recompile the document with pdfLATEX. This step compiles the quick-start document to PDF, this time including the SAS results that were generated in the preceding step. In some cases listing outputs may not be framed properly after this step. If your listing outputs are not framed properly, repeat this step so that LaTeX can remeasure the listing outputs.

Please refer to the following file for detailed information:

Click to access statrepmanual.pdf

 

Useful for referring—02-20-2015

 

• Deep Learning Master Class
• Advances in Variational Inference
• Numerical Optimization: Understanding L-BFGS
• An exact mapping between the Variational Renormalization Group and Deep Learning
• New ASA Guidelines for Undergraduate Statistics Programs
• 奇异值分解（We Recommend a Singular Value Decomposition）
• 如何简单形象又有趣地讲解神经网络是什么？
• Academic vs. Industry Careers
• Hadley Wickham: Impact the world by being useful
• Statisticians in World War II: They also served
• A Brief Overview of Deep Learning
• Advice for applying Machine Learning
• Deep Learning Tutorial
• Gibbs Sampling in Haskell
• How-to go parallel in R – basics + tips

 

Useful for referring—12-04-2014

 

• Tutorial: How to detect spurious correlations, and how to find the …
• Practical illustration of Map-Reduce (Hadoop-style), on real data
• Jackknife logistic and linear regression for clustering and predict…
• From the trenches: 360-degrees data science
• A synthetic variance designed for Hadoop and big data
• Fast Combinatorial Feature Selection with New Definition of Predict…
• A little known component that should be part of most data science a…
• 11 Features any database, SQL or NoSQL, should have
• Clustering idea for very large datasets
• Hidden decision trees revisited
• Correlation and R-Squared for Big Data
• Marrying computer science, statistics and domain expertize
• New pattern to predict stock prices, multiplies return by factor 5
• What Map Reduce can’t do
• Excel for Big Data
• Fast clustering algorithms for massive datasets
• Source code for our Big Data keyword correlation API
• The curse of big data
• How to detect a pattern? Problem and solution
• Interesting Data Science Application: Steganography
• Easily create documents from R with Rmarkdown
• How to publish R and ggplot2 to the web
• magrittr: Simplifying R code with pipes
• Updated dplyr Examples
• Video introduction to data manipulation with dplyr
• R and Data Science
• jiebaR中文分词——R的灵活，C的效率
• Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
• 41 hours of courses given in Iceland this Summer at the Machine Learning Summer School.
• summary of parallel machine learning approaches
• big data and data science talks

 

Useful for referring—9-11-2014

• Some R Resources for GLMs
• 失联搜救中的统计数据分析
• The gap between data mining and predictive models
• Data Mining, machine learning and statistics.
• useR! 2014 is underway with 16 tutorials
• What is Scalable Machine Learning?
• rlist：基于list在R中处理非关系型数据
• The perfect candidate
• The Leek group guide to giving talks
• 38 Seminal Articles Every Data Scientist Should Read
• Deep Learning – important resources for learning and understanding
• Twenty rules for good graphics     +   Ten Simple Rules for Better Figures
• Git Cookbook
• Making Your Code Citable
• biblatex for statisticians
• Do your “data janitor work” like a boss with dplyr

Useful for referring—2-25-2014

1. Interview with Nick Chamandy, statistician at Google
2. You and Your Research +  video
3. Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained
4. A Survival Guide to Starting and Finishing a PhD
5. Six Rules For Wearing Suits For Beginners
6. Why I Created C++
7. More advice to scientists on blogging
8. Software engineering practices for graduate students
9. Statistics Matter
10. Getting Comfortable in the Terminal: Linux
11. What statistics should do about big data: problem forward not solution backward
12. How signals, geometry, and topology are influencing data science
13. The Bounded Gaps Between Primes Theorem has been proved
14. A non-comprehensive list of awesome things other people did this year.
15. Jake VanderPlas writes about the Big Data Brain Drain from academia.
16. Tomorrow’s Professor Postings
17. Best Practices for Scientific Computing
18. Some tips for new research-oriented grad students
19. 3 Reasons Every Grad Student Should Learn WordPress
20. How to Lie With Statistics (in the Age of Big Data)
21. The Geometric View on Sparse Recovery
22. The Mathematical Shape of Things to Come
23. A Guide to Python Frameworks for Hadoop
24. Statistics, geometry and computer science.
25. How to Collaborate On GitHub
26. Step by step to build my first R Hadoop System
27. Open Sourcing a Python Project the Right Way
28. Data Science MD July Recap: Python and R Meetup
29. git 最近感悟
30. 10 Reasons Python Rocks for Research (And a Few Reasons it Doesn’t)
31. Effective Presentations – Part 2 – Preparing Conference Presentations
32. Doing Statistical Research
33. How to Do Statistical Research
34. Learning new skills
35. How to Stand Out When Applying for An Academic Job
36. Maturing from student to researcher
37. False discovery rate regression (cc NSA’s PRISM)
38. Job Hunting Advice, Pt. 3: Networking
39. Getting Started with Git

Useful for referring–1-20-2013

1. Machine Learning, Big Data, Deep Learning, Data Mining, Statistics, Decision & Risk Analysis, Probability, Fuzzy Logic FAQ
2. A Funny Thing Happened on the Way to Academia . . .
3. Advice for students on the academic job market (2013 edition)
4. Perspective: “Why C++ Is Not ‘Back’”
5. Is Fourier analysis a special case of representation theory or an analogue?
6. The Beauty of Bioconductor
7. The State of Statistics in Julia
8. Open Source Misfeasance
9. Book review: The Signal and The Noise
10. Should the Cox Proportional Hazards model get the Nobel Prize in Medicine?
11. The most influential data scientists on Twitter
12. Here is an interesting review of Nate Silver’s book. The interesting thing about the review is that it doesn’t criticize the statistical content, but criticizes the belief that people only use data analysis for good. This is an interesting theme we’ve seen before. Gelman also reviews the review.—–Simply Statistics
13. Video : “Matrices and their singular values” (1976)
14. Beyond Computation: The P vs NP Problem – Michael Sipser—-This talk is arguably the very best introduction to computational complexity .
15. What are some of your personal guidelines for writing good, clear code?
16. How do you explain Machine learning and Data Mining to non CS people?
17. Suggested New Year’s resolution: start a blog:  A blog forces you to articulate your thoughts rather than having vague feelings about issues; You also get much more comfortable with writing, because you’re doing it rather than thinking about doing it; If other people read your blog you get to hear what they think too. You learn a lot that way. || Set aside time for your blog every day. Keep notes for yourself on bloggy subjects (write a one-line gmail to yourself with the subject “blog ideas”).
18. The most influential data scientists on Twitter
19. Tips on job market interviews
20. The age of the essay

Useful for referring–11-28-2012

1. Grad Student’s Guide to Good Coffee+Grad Student’s Guide to Good Tea
2. Favorite Apps for Work and Life
3. estimating a constant (not really)
4. Reinforcement Learning in R: An Introduction to Dynamic Programming
5. The Future of Machine Learning (and the End of the World?)
6. 10 Papers Every Programmer Should Read (At Least Twice)
7. R in the Press
8. On Chomsky and the Two Cultures of Statistical Learning
9. Speech Recognition Breakthrough for the Spoken, Translated Word
10. Frequentist vs Bayesian
11. w4s – the awesomeness we’re experiencing
12. Why is the Gaussian so pervasive in mathematics?
13. C++ Blogs that you Regularly Follow
14. An interview with Brad Efron about scientific writing. I haven’t watched the whole interview, but I do know that Efron is one of my favorite writers among statisticians.
15. Slidify, another approach for making HTML5 slides directly from R.  (1) It is still just a little too hard to change the theme/feel of the slides (2) The placement/insertion of images is still a little clunky, Google Docs has figured this out, if they integrated the best features of Slidify, Latex, etc. into that system, it will be great.
16. Statistics is still the new hotness. Here is a Business Insider list about 5 statistics problems that will“change the way you think about the world”.
17. New Yorker, especially the line,”statisticians are the new sexy vampires, only even more pasty” (via Brooke A.)
18. The closed graph theorem in various categories
19. Got spare time? Watch some videos about statistics
20. About the first Borel-Cantelli lemma
21. Yihui Xie—-The Setup
22. Best Practices for Scientific Computing

Useful for referring–9-23-2012

1. Towards Better PDF Management with the Filesystem
2. What is life like for PhDs in computer science who go into industry?
3. Online REPL for 17 programming languages
4. Logistic regression vs. multiple regression—–Many statisticians seem to advise the use of logistic regression over multiple regression by invoking this logic: “A probability value can’t exceed 1 nor can it be less than 0. Since multiple regression often yields values less than 0 and greater than 1, use logistic regression.” While we can understand this argument, our feeling is that, in the applied fields we toil in, that argument is not a very practical one. In fact a seasoned statistics professor we know says (in effect): “What’s the big deal? If multiple regression yields any predicted values less than 0, consider them 0. If multiple regression yields any values greater than 1, consider them 1. End of story.” We agree.
5. Scientific Python
6. An everyday essential: the timer+My personal productivity rules
7. Bill Thurston—by Terrace Tao; Bill Thurston, 1946-2012—by Peter Woit; Bill Thurston 1946-2012—by David Speyer.
8.  Surviving a PhD: 10 top tips that shows how to survive your PhD
9. How different PhD’s work:Differences and similarities between departments about PhD process
10. Countdown Begins: Countdown starts for submission of the thesis
11. PhD Life is Wonderful:Doing PhD at Warwick University is a wonderful experience
12. Too Many Emails In Your Inbox: Use Outlook folders to manage your emails
13. Introduction to REX Facility: Videos for introducing Wolfson Research Exchange and its facilities
14. Power of Supervisors: Control,inner happiness and optimisim
15.  Unorthodox Tools of a Researcher: Reflection and examples of unorthodox tools that helps you PhD period
16. Homesickness and Culture Clashes: Homesickness of international students and cultural differences
17. Choosing Your PhD Examiners: Tips for choosing the relevant examiners for PhD Viva
18. Effective Research Tools: Examples of useful research tools
19. PhD,Risks and Murphy’s Law: “Anything that can go wrong will go wrong” according to Murphy’s Law
20. Will Data Scientists Be Replaced by Tools?
21. Update: TeX Writer for iPad (+ LaTeX + AMS)
22. Why physicists like models, and why biologists should
23. The ENCODE project: lessons for scientific publication
24. Perspectives From A Postdoc: What is a Postdoc?
25. Chris Blattman gives advice on PhD students’ NSF applications
26. ENCODE floods the news networks…
27. Maybe mostly useful for me, but for other people with Tumblr blogs, here is a way to insert Latex.—From Simply Statistics
28. Harvard Business school is getting in on the fun, calling the data scientist the sexy profession for the 21st century. Although I am a little worried that by the time it gets into a Harvard Business document, the hype may be outstripping the real promise of the discipline. Still, good news for statisticians! (via Rafa via Francesca D.’s Facebook feed).—From Simply Statistics
29. The counterpoint is this article which suggests that data scientists might be able to be replaced by tools/software. I think this is also a bit too much hype for my tastes. Certain things will definitely be automated and we may even end up with a deterministic statistical machine or two. But there will continually be new problems to solve which require the expertise of people with data analysis skills and good intuition (link via Samara K.)—From Simply Statistics