[talks] Colloquium Speaker Roger Grosse, TODAY April 7th- 12:30pm

[Mitra D. Kelly]

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.

 

 

Mitra Kelly

Academic Secretary

Princeton University

Computer Science Dept

35 Olden Street

Princeton NJ 08540

mkelly at cs.princeton.edu <mailto:mkelly at cs.princeton.edu> 

609-258-4562

 

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