###### tags: `Supervision`

# Agenda for 16.04.2021 supervision meeting

* What we discuss mainly: Survey on Multi-Fairness.
* Study the Multi-Objective Fairness papers and try to organize and write a survey.
* Also the direction of "AdaFair, FAHT, Adwin, Gradient Boosting" is to be maintained.
* Arjun wants to know my idea about the Min-Max ICML 2020 paper and other MOA in Fairness.
* The new paper that Eirini introduced will be discussed : "A generic algorithm for reducing bias in parametric estimation".
* Grant Agreement and our next movement for the grant
## Zotero Library:
I created a library in zotero of almost All new MO-Fairness papers.
* "Multi-Objective_Fairness_Learning"

This main folder is for survey papers. here is the link for the Library: https://www.zotero.org/groups/2844476/phd_literature/collections/IHK5CB8Y
* There is a sub-folder: "MOFariness Applications" including MO-Fariness papers in the Industry or Economics
* Another sub-category is the "Multi-Objective Machine Learning" that includes newest highly impact publications in top proceedings about MOAs in different Machine Learning areas.
* Reinforcement Learning
* Multi-Task Learning
* Multi-Objective Gradient Descent

## Reference Books
* Understanding Machine Learning: From Theory to Algorithms
* Shai Shalev-Shwartz
* An Introduction to Statistical Learning
* Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
* https://www.statlearning.com/
* Deep Learning
* Aaron Courville, Ian Goodfellow, and Yoshua Bengio
* Fairness and machine learning
* https://fairmlbook.org/
* Solon Barocas, Moritz Hardt, Arvind Narayanan
* Convex Optimization
* Stephen Boyd
* Mathematics for Machine Learning
* Marc Peter Deisenroth
* Probably a book for Online Learning
* A reference book for Multi-Objective Optimization

## Courses:
* Now I am catching up with Andrew NG's online ML course
* I might need to attend an online learning course. If you know a good one