Twitch streaming

(Coffee)I am streaming live DJ sets on Twitch: https://twitch.tv/chato_chateaux

I've played mostly tech house and deep house, with some pop, accompanied by movie clips or pixel art. Take a look at these these clips and the setlists of each session.

Christmas message

My Christmas message is this: if you measured some quantity with a certain precision (say 2 decimal places) and obtained some trailing zeroes, please don't remove them. Don't mix in a table "0.5" and "0.48". Write "0.50" and "0.48" because that is what you measured. Ho ho ho.

My car!

It was just a brief moment so I left the car doors unlocked while I was buying something. I kept looking at the car several times to see if someone was going near it, but when I left the store, the car was not there. It had been stolen! I thought while I ran desperately. Then I woke up. As any normal person, I thought "I've to get out of bed and go check if my car is still there." The thing is, I don't have a car.

New tools for fair ranking available

With the support of a Data Transparency Lab grant, working with Meike Zehlike and Tom Sühr from TU Berlin, and Ivan Kitanovski from Ss. Cyril and Methodius University of Skopje, we have produced new tools for creating fair rankings.

Reference for both tools:
Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski: "FairSearch: A Tool For Fairness in Ranked Search Results". arXiv:1905.13134 (2019). Homepage: https://github.com/fair-search

FA*IR: fair ranking by post-processing

The first set of tools correspond to the FA*IR paper in CIKM 2017, which describes a method for ranking post-processing based on a statistical test called the ranking group fairness condition:

Reference for the FA*IR algorithm:
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: "FA*IR: A Fair Top-k Ranking Algorithm". Proc. of the 2017 ACM on Conference on Information and Knowledge Management (CIKM).

DELTR: fair ranking in-processing by learning-to-rank

The second set of tools correspond to an unpublished work on Learning To Rank (LTR) while reducing disparate impact, an in-processing algorithm named DELTR:

Reference for the DELTR algorithm:
Meike Zehlike, Gina-Theresa Diehn, Carlos Castillo. "Reducing Disparate Exposure in Ranking: A Learning to Rank Approach" arXiv:1805.08716 (2018).

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