[Ml-stat-talks] Fwd: [talks] Subject: Colloquium Speaker Roger Grosse, Thursday April 7th- 12:30pm

Barbara Engelhardt bee at princeton.edu
Thu Mar 31 09:42:00 EDT 2016


Talk of interest next week.

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Colloquium Speaker
Roger Grosse, the University of Toronto

April 7th- 12:30pm



Computer Science 105


Exploiting compositionality to explore a large space of model structures



I will present flexible algorithms for model discovery and model fitting
which apply to broad, open-ended classes of models, yet which also
incorporate model-specific algorithmic insights. First, I will introduce a
framework for building probabilistic models compositionally out of common
modeling motifs, such as clustering, sparsity, and dimensionality
reduction. This compositional framework yields a variety of existing models
as special cases. We can flexibly perform posterior inference across this
large, open-ended space of models by composing sophisticated inference
algorithms carefully designed for the individual modeling motifs. An
automatic structure search procedure over this space of models yields
sensible analyses of datasets as diverse as motion capture, natural image
patches, and Senate voting records, all using a single software package
with no hand-tuned metaparameters. Applying a similar compositional
structure search procedure to Gaussian Process models yields interpretable
decompositions of diverse time series datasets and enables automatic
generation of natural language reports.  Finally, compositional structure
search depends crucially on the estimation of intractable likelihoods. I
will briefly outline an approach for obtaining precise likelihood estimates
with rigorous tail bounds by sandwiching the true value between stochastic
upper and lower bounds.



BIO: Roger Grosse is a Postdoctoral Fellow in the University of Toronto
machine learning group. He received his Ph.D. in computer science from MIT
under the supervision of of Bill Freeman. He is a recipient of the NDSEG
Graduate Fellowship, the Banting Postdoctoral Fellowship, and outstanding
paper awards at the International Conference of Machine Learning (ICML) and
the Conference for Uncertainty in AI (UAI). He is also a co-creator of
Metacademy, an open-source web site for developing personalized learning
plans in machine learning and related fields.
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