Wednesday, May 13, 2015

Tonight! Paris Machine Learning Meetup #9, Season 2: ML @Quora and @Airbus and in HFT, Tax, APIs war

The Paris Machine Learning meetup was streamed on two different platforms from two different cameras. The first one was brought to us by Gerard Pazuelo. Here is the result:


And then there was the traditional google hangout on air below. The presentations are available below. The meetup was in French.

Here is the program with slides (all the slides should be here by the time the meetup starts at 6:45pm Paris time)

From understanding the quality of user generated content, to providing personalized reading experiences for its users, Quora faces many important questions that can be addressed by machine learning. In this talk, I will describe some of these problems, and the machine learning solutions we've built to solve them.

+ Christophe Bourgignat, AXA, remise les prix du dernier concours AXA
We propose an optimization framework for market-making in a limit-order book, based on the theory of stochastic approximation. We consider a discrete-time variant of the Avellaneda-Stoikov model [1] similar to its developent in the article of Laruelle, Lehalle and Pagès [9] in the context of optimal liquidation tactics. The idea is to take advantage of the iterative nature of the process of updating bid and ask quotes in order to make the algorithm optimize its strategy on a trial-and-error basis (i.e. on-line learning). An advantage of this approach is that the exploration of the system by the algorithm is performed in run-time, so explicit specifications of the price dynamics are not necessary, as is the case in the stochastic control approach [7]. As it will be discussed, the rationale of our method can be extended to a wider class of algorithmic-trading tactical problems other than market-making.

+ Gerard Dupont, Airbus Defense and Space

Traitements avancés de flux documentaires multimédia chez AIRBUS DS - pourquoi et comment. Unstructured data processing – why ? How ?  Practical machine learning for intelligence applications
If machine learning is coming up with tremendous results in the recent days, its application to unstructured data processing is still a struggle. When google is not enough and the need for a specialized web intelligence system arises, the numerous constraints of the real world of web data are hitting hard. The talk will go through an overview of this domain, its challenges, how ML is - currently - put in use and what are the foreseen next steps.

Amazon ML made a lot of noise when it came out last month. Shortly afterwards, someone posted a link to Google Prediction API on HackerNews and it quickly became one of the most popular posts (although Google's API was released back in 2010). Both services provide similar APIs, which gave me the idea of comparing them. For that, I used the Kaggle “give me some credit” challenge. But I didn’t stop there: I also included startups who provide competing APIs in this comparison — namely, PredicSis and BigML. In this wave of new ML services, the giant tech companies are getting all the headlines, but bigger companies do not necessarily have better products...

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