MLSP 2016 - i.e. the IEEE International Workshop on Machine Learning for Signal Processing - was a great, well-organised workshop, held last week on Italy's Amalfi coast. (Yes, lovely place to go for work - if only I'd had some spare time for sightseeing on the side! Anyway.)
Here are a few of the papers that caught my interest:
- Approximate State-Space Gaussian Processes Via Spectral Transformation by Toni Karvonen and Simo SÃ¤rkkÃ¤. This is an important contribution to the current work on Gaussian processes and in particular on running efficient Gaussian process inference. It builds on other work from the SÃ¤rkkÃ¤ lab converting Gaussian processes to state-space models, which often involves a (mild) approximation. This paper introduces some new methods in that vein, with proofs, and in fact the paper includes various ways to approximate a GP. A veritable mathematical toolkit. It seems the Taylor expansion (the most immediately comprehensible IMHO) is not the best.
Actually, there was substantial work involving Gaussian processes at MLSP. Is it a growth area? Well, if the use of GPs can be made more scalable (as in the above paper) then yes, it certainly should be. They are a very flexible and general tool, and nicely Bayesian too. Richard Turner's keynote about Gaussian processes was a beautiful introduction - he manages to make GPs extremely understandable. If you get a chance to see him speak on them then do.
- Localizing Users And Items From Paired Comparisons by O'Shaughnessy and Davenport. This is a nicely conceived addition to the literature on recommendation algorithms, and with good demonstrations of how the approach is robust to issues such as incoherent paired comparisons.
- "Data Privacy Protection By Kernel Subspace Projection And Generalized Eigenvalue Decomposition" by Diamantaras and Kung. Privacy-preserving computing is an important area for current research. It's made obvious when we see how much a large company like Facebook or Tesco can infer about its users. Here, the authors treat privacy as a classification task - i.e. the data to be kept private is some kind of discrete label - and they apply an LDA-like method: maximise the scatter between the target classes for the "allowed" task, while minimising the scatter between the private classes. (I raised an issue with their "Privacy Index", noting that the desired accuracy for the private task was not in fact zero but ignorance. I'd presume that a metric based on mutual information would be a nice alternative.)
- "Scale and shift invariant time/frequency representation using auditory statistics: application to rhythm description" by Marchand and Peeters. They use the "Scale Transform", a class of Mellin transform. Equivalent to exponentially time-warping a signal then weighting by an exponential window. Since it's not shift-invariant you don't want to apply it directly to audio, but to e.g. autocorrelation. From there, they argue you get a good featureset for characterising musical rhythm.
- Score-Matching Estimators For Continuous-Time Point-Process Regression Models by Sahani, Bohner and Meyer - good to see this. I've been using point process models to analyse bird communication and so I'm interested in efficient ways to do such analysis, which commonly seem to come from the computational neuroscience literature at the moment. Notable that this approach doesn't require any time discretisation, so could be useful. The functions analysed need to be differentiable, so to work with impulsive time series they actually convolve/correlate them with basis functions; feels like a minor hack but there you go.
Also, I was very pleased that Pablo A Alvarado Duran presented his work on Gaussian processes for music audio modelling - his first publication as part of his PhD with me!