Annual Sobel Lecture, Professor Bin Yu (May 10)

[Haraldur Tómas Hallgrímsson]

Hi all,

The below talk held tomorrow might be of interest.

Cheers,

Haraldur

Wednesday, May 10, Location: Multi-Cultural Center, 3:30-5:00 p.m.;
refreshments served at 3:15 p.m.

Speaker: Bin Yu (Statistics and EECS, UC Berkeley) [Sobel Seminar]

Title: Three principles of data science: predictability, stability, and
computability

Abstract: In this talk, I'd like to discuss the intertwining importance and
connections of three principles of data science in the title in data-driven
decisions. Making prediction as its central task and embracing computation
as its core, machine learning has enabled wide-ranging data-driven
successes. Prediction is a useful way to check with reality. Good
prediction implicitly assumes stability between past and future. Stability
(relative to data and model perturbations) is also a minimum requirement
for interpretability and reproducibility of data driven results (cf. Yu,
2013). It is closely related to uncertainty assessment. Obviously, both
prediction and stability principles can not be employed without feasible
computational algorithms, hence the importance of computability.

The three principles will be demonstrated in the context of two
neuroscience projects and through analytical connections. In particular,
the first project adds stability to predictive modelling used for
reconstruction of movies from fMRI brain signals for interpretable models.
The second project uses predictive transfer learning that combines AlexNet,
GoogleNet and VGG with single V4 neuron data for state-of-the-art
prediction performance. Our results lend support, to a certain extent, to
the assemblence of these CNNs to brain and at the same time provide stable
pattern interpretations of neurons in the difficult primate visual cortex
V4.