Attending S4D

01 Mar 2018

I will be attending Research Summer School on Statistics for Data Science at Caen University in Caen, France. I will update this post on the matter.

Reading list for S4D:

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On the subject of ML optimizations:

1. Schoenauer-Sebag, A., Schoenauer, M., & Sebag, M. (2017). 2. Stochastic Gradient Descent: Going As Fast As Possible But Not Faster. arXiv preprint arXiv:1709.01427.
2. França, G., & Bento, J. ADMM and Random Walks on Graphs.
3. https://towardsdatascience.com/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f
4. https://arxiv.org/pdf/1712.07897.pdf
5. https://arxiv.org/pdf/1706.10207.pdf
6. https://towardsdatascience.com/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f

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On the subject of Probabilistic Modeling for ML:

1. http://mlg.eng.cam.ac.uk/zoubin/talks/mit12csail.pdf

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On the subject of Theory of Statistical Inference:

1. http://www.ltcc.ac.uk/media/london-taught-course-centre/documents/Fundamental-Theory-of-Statistical-Inference-notes.pdf

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On the subject of MM algorithms:

1. https://www.stat.berkeley.edu/~aldous/Colloq/lange-talk.pdf
2. https://arxiv.org/abs/1506.07613
3. http://www.ece.ust.hk/~palomar/ELEC5470_lectures/11/slides_MM_algorithms.pdf
4. https://www.stat.berkeley.edu/~aldous/Colloq/lange-talk.pdf

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