ECML-PKDD 2015 Review

ECML-PKDD was a delight this year. Porto is definitely on the short list of the best European cities in which to have a conference. The organizers did a wonderful job injecting local charm into the schedule, e.g., the banquet at Taylor's was a delight. It's a wine city, and fittingly wine was served throughout the conference. During the day I stuck to coffee: jet lag, soft lights, and whispered mathematics are sedating enough without substituting coffee for alcohol. There is no question, however, that poster sessions are far better with a bit of social lubrication.

The keynotes were consistently excellent. Some standouts for me were:

• Pedros Domingos presented his latest take on sum-product networks as a class of nonconvex functions for which finding a global maximum is tractable. Machine learning was (is?) obsessed with convex functions because it is a large class for which finding the global maximum is tractable. Lately the deep learning community has convincingly argued that convexity is too limiting, and as a result we are all getting more comfortable with more ``finicky'' optimization procedures. Perhaps what we need is a different function class?
• Hendrik Blockeel talked about declarative machine learning. I work in a combination systems-ML group and I can tell you systems people love this idea. All of them learned about how relational algebra ushered in a declarative revolution in databases via SQL, and see the current state of affairs in machine learning as a pre-SQL mess.
• Jure Leskovec did an unannounced change of topic, and delivered a fabulous keynote which can paraphrased as: ``hey you machine learning people could have a lot of impact on public policy, but first you need to understand the principles and pitfalls of counterfactual estimation.'' I couldn't agree more, c.f., Gelman. (Jure also gave the test-of-time paper talk about Kronecker graphs.)
• Natasa Milic-Frayling detailed (with some disdain) the miriad of techniques that digital web and mobile advertising firms use to track and profile users. It was all very familiar because I worked in computational advertising for years, but the juxtaposition of the gung-ho attitude of ad networks with the European elevated respect for privacy was intriguing from a sociological perspective.

There were also some papers with which I'm going to spend quality time.

• Half-Space Mass: A Maximally Robust and Efficient Data Depth Method. Statisticians have been ruminating for years on how to extend the concept of median to multidimensional data sets. Half-Space Mass is a slight tweak of Tukey's half-space depth which has many of the desirable properties and yet is easy to estimate via sampling. This is potentially relevant to multiple unsupervised scenarios, e.g., anomaly detection.
• Superset Learning Based on Generalized Loss Minimization. This is a different way of thinking about label uncertainty which yields several familiar techniques, but also some new ones.

LearningSys NIPS Workshop CFP

CISL is the research group in which I work at Microsoft. The team brings together systems and machine learning experts, with the vision of having these two disciplines inform each other. This is also the vision for the LearningSys workshop, which was accepted for NIPS 2015, and is co-organized by Markus Weimer from CISL.

If this sounds like your cup of tea, check out the CFP and consider submitting your work.

Also, CISL is hiring: so if this is really your cup of tea, send your resume to me (to the address in top-right corner of my blog); or introduce yourself at the workshop in Montreal.