Paris Machine Learning Meetup #5 Season 4: LIME 'Why should I trust you', Apache SAMOA, GAN for Cracks, Opps, NIPS2016

  Video streaming of the event is here:

 

Mobiskill nous invitent dans ces nouveaux locaux. Voici le programme pour le meetup, si vous avez des annonces ou meme une presentation en plus, n'hésitez pas a remplir ce formulaire.


La salle aura une capacité de 120 personnes. La salle ouvrira ces portes avant 19h00.

On parlera mettre un sens aux données trop grandes, l'utlisation de GAN (qui ont fait fureur à NIPS), de crowdsourcing d'opportunités et si on a le temps de ce qui passe a NIPS à Barcelone.
Marco and Albert are likely to speak English while Julien, Daniel et Igor should be speaking French.


Marco Tulio Ribeiro, "Why Should I Trust You?" Explaining the Predictions of Any Classifier "
[code] arxiv link (short video presentation and longer KDD presentation)

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.

Albert Bifet, Telecom-Paristech, "Apache SAMOA"  Github repo

In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance.  Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.                                     

Julien Launay  "Cracking Crack Mechanics: Using GANs to replicate and learn more about fracture patterns" without animation link is here 

When modeling transfers through a medium in civil engineering, knowing the precise influence of cracks is often complicated, doubly so since the transfer and fracture problems are often heavily linked. I will present a new way to generate “fake” cracking patterns using GANs, and will then expand on how such novel techniques can be used to learn more about fracture mechanics.                                      

Daniel Benoilid   , foulefactory.com,    5 min talk "Man + Machine : Crowdsourcing opportunities"
  

How you can leverage on crowdsourcing to earn time on learning phases and provide a fall back in real time when the confidence interval isn't good.

Igor Carron, "So what happened at NIPS2016 ?"

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