Archive for machine learning

assistant/associate professor position in statistics/machine-learning at ENSAE

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , on March 10, 2020 by xi'an

ENSAE (my Alma Mater) is opening a new position for next semester in statistics or/and machine-learning. At the Assistant Professor level, the position is for an initial three-year term, renewable for another three years, before the tenure evaluation. The school is located on the Université Paris-Saclay campus, only teaches at the Master and PhD levels, and the deadline for application is 31 March 2020. Details and contacts on the call page.

Julyan’s talk on priors in Bayesian neural networks [cancelled!]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on March 5, 2020 by xi'an

Next Friday, 13 March at 1:30p.m., Julyan Arbel, researcher at Inria Grenoble will give a All about that Bayes talk at CMLA, ENS Paris-Saclay (building D’Alembert, room Condorcet, Cachan, RER stop Bagneux) on

Understanding Priors in Bayesian Neural Networks at the Unit Level

We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to induce an L², “weight decay”, regularization. Our results characterize a more intricate regularization effect at the level of the unit activations. Our main result establishes that the induced prior distribution on the units before and after activation becomes increasingly heavy-tailed with the depth of the layer. We show that first layer units are Gaussian, second layer units are sub-exponential, and units in deeper layers are characterized by sub-Weibull distributions. Our results provide new theoretical insight on deep Bayesian neural networks, which we corroborate with simulation experiments.

 

Gabriel’s talk at Warwick on optimal transport

Posted in Statistics with tags , , , , , , on March 4, 2020 by xi'an

Irène Waldspurger, CNRS bronze medal

Posted in Statistics with tags , , , , , , on February 14, 2020 by xi'an

My colleague at Paris Dauphine, Irène Waldspurger, got one of the prestigious CNRS bronze medals this year. Irène is working on inverse problems and machine learning, with applications to sensing and imaging. Congrats!

off to BayesComp 20, Gainesville

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on January 7, 2020 by xi'an

postdoc in Bayesian machine learning in Berlin [reposted]

Posted in R, Statistics, Travel, University life with tags , , , , , , , , , , , , on December 24, 2019 by xi'an

The working group of Statistics at Humboldt University of Berlin invites applications for one Postdoctoral research fellow (full-time employment, 3 years with extension possible) to contribute to the research on mathematical and statistical aspects of (Bayesian) learning approaches. The research positions are associated with the Emmy Noether group Regression Models beyond the Mean – A Bayesian Approach to Machine Learning and working group of Applied Statistics at the School of Business and Economics at Humboldt-Universität Berlin. Opportunities for own scientific qualification (PhD)/career development are provided, see an overview and further links. The positions are to be filled at the earliest possible date and funded by the German Research Foundation (DFG) within the Emmy Noether programme.

Requirements:
– an outstanding PhD in Statistics, Mathematics, or related field with specialisation in Statistics, Data Science or Mathematics;
– a strong background in at least one of the following fields: mathematical statistics, computational methods, Bayesian statistics, statistical learning, advanced regression modelling;
– a thorough mathematical understanding.
– substantial experience in scientific programming with Matlab, Python, C/C++, R or similar;
– strong interest in developing novel statistical methodology and its applications in various fields such as economics or natural and life sciences;
– a very good communication skills and team experience, proficiency of the written and spoken English language (German is not obligatory).

Opportunities:
We offer the unique environment of young researchers and leading international experts in the fields. The vibrant international network includes established collaborations in Singapore and Australia. The positions offer potential to closely work with several applied sciences. Information about the research profile of the research group and further contact details can be found here. The positions are paid according to the Civil Service rates of the German States “TV-L”, E13 (if suitably qualified).

Applications should include:
– a CV with list of publications
– a motivational statement (at most one page) explaining the applicant’s interest in the announced position as well as their relevant skills and experience
– copies of degrees/university transcripts
– names and email addresses of at least two professors that may provide letters of recommendation directly to the hiring committee Applications should be sent as a single PDF file to: Prof. Dr. Nadja Klein (nadja.klein[at]hu-berlin.de), whom you may also contact for questions concerning this job post. Please indicate “Research Position Emmy Noether”.

Application deadline: 31st of January 2020

HU is seeking to increase the proportion of women in research and teaching, and specifically encourages qualified female scholars to apply. Severely disabled applicants with equivalent qualifications will be given preferential consideration. People with an immigration background are specifically encouraged to apply. Since we will not return your documents, please submit copies in the application only.

no dichotomy between efficiency and interpretability

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , on December 18, 2019 by xi'an

“…there are actually a lot of applications where people do not try to construct an interpretable model, because they might believe that for a complex data set, an interpretable model could not possibly be as accurate as a black box. Or perhaps they want to preserve the model as proprietary.”

One article I found quite interesting in the second issue of HDSR is “Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition” by Cynthia Rudin and Joanna Radin, which describes the setting of a NeurIPS competition last year, the Explainable Machine Learning Challenge, of which I was blissfully unaware. The goal was to construct an operational black box predictor fpr credit scoring and turn it into something interpretable. The authors explain how they built instead a white box predictor (my terms!), namely a linear model, which could not be improved more than marginally by a black box algorithm. (It appears from the references that these authors have a record of analysing black-box models in various setting and demonstrating that they do not always bring more efficiency than interpretable versions.) While this is but one example and even though the authors did not win the challenge (I am unclear why as I did not check the background story, writing on the plane to pre-NeuriPS 2019).

I find this column quite refreshing and worth disseminating, as it challenges the current creed that intractable functions with hundreds of parameters will always do better, if only because they are calibrated within the box and have eventually difficulties to fight over-fitting within (and hence under-fitting outside). This is also a difficulty with common statistical models, but having the ability to construct error evaluations that show how quickly the prediction efficiency deteriorates may prove the more structured and more sparsely parameterised models the winner (of real world competitions).