Van Dantzig Seminar

nationwide series of lectures in statistics

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Next Van Dantzig Seminar: 5 april 2019

Speakers: Paul Fearnhead, Chris Oates, Lorenzo Rosasco

Programme: (click names or scroll down for titles and abstracts)

14:00 - 15:00 Lorenzo Rosasco (Universita di Genova, MIT)
15:00 - 15:20 Break
15:20 - 16:20 Chris Oates (Newcastle University)
16:20 - 16:40 Break
16:40 - 17:40 Paul Fearnhead (University of Lancaster)
17:40 - Reception
Location: VU Amsterdam, room WN-C624 (Wis- en natuurkundegebouw)

Titles and abstracts

  • Lorenzo Rosasco

    Unconventional regularization for efficient and sustainable machine learning

    Classic algorithm design is based on penalizing or imposing explicit constraints to an empirical objective function, which is eventually optimized. In practice, however, a number of different algorithmic solutions are employed. Their effect on final performance is hard to assess a priori and typically done empirically. In this talk, I will consider a nonparametric linear least squares framework and will take a regularization perspective to understand the effect of two commonly used ideas: sketching and iterative optimization. This analysis will highlight the role and the interplay of different algorithmic choices, including training time, step-size, mini-batch size, and the choice of sketching, among others. Indeed, one can view all these choices as implementing a form of “algorithmic regularization”. The obtained results provide new and practical guidelines for algorithm design. They suggest that optimal statistical accuracy can be achieved while dramatically improving computational efficiency. Theoretical findings will be illustrated in the context of large scale kernel methods, where we developed the first solvers able to scale to millions of training points.

  • Paul Fearnhead

    Efficient Approaches to Changepoint Problems with Dependence Across Segments

    Changepoint detection is an increasingly important problem across a range of applications. It is most commonly encountered when analysing time-series data, where changepoints correspond to points in time where some feature of the data, for example its mean, changes abruptly. Often there are important computational constraints when analysing such data, with the number of data sequences and their lengths meaning that only very efficient methods for detecting changepoints are practically feasible. A natural way of estimating the number and location of changepoints is to minimise a cost that trades-off a measure of fit to the data with the number of changepoints fitted. There are now some efficient algorithms that can exactly solve the resulting optimisation problem, but they are only applicable in situations where there is no dependence of the mean of the data across segments. Using such methods can lead to a loss of statistical efficiency in situations where e.g. it is known that the change in mean must be positive. This talk will present a new class of efficient algorithms that can exactly minimise our cost whilst imposing certain constraints on the relationship of the mean before and after a change. These algorithms have links to recursions that are seen for discrete-state hidden Markov Models, and within sequential Monte Carlo. We demonstrate the usefulness of these algorithms on problems such as detecting spikes in calcium imaging data. Our algorithm can analyse data of length 100,000 in less than a second, and has been used by the Allen Brain Institute to analyse the spike patterns of over 60,000 neurons. This is joint work with Toby Hocking, Sean Jewell, Guillem Rigaill and Daniela Witten.

  • Chris Oates

    Stein's Method in Computational Statistics

    This is a talk on computational Bayesian statistics; in particular, we aim to show how the judicious use of Stein's method enables generation of low-discrepancy point sets for posterior approximation, as well as rate-optimal approximation of posterior integrals of Sobolev functions.

Van Dantzig Seminar

The Van Dantzig seminar is a nationwide series of lectures in statistics, which features renowned international and local speakers, from the full width of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the "Theory of Collective Phenomena" (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is supported financially by among others the STAR cluster and the Section Mathematical Statistics of the VVS-OR.

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