Archive for conference
ICWES18 in Warwick [11-14 September 2020]
Posted in Statistics with tags Britain, conference, Coventry, Engineering and Medicine, Faculty of Science, ICWES18, International Conference of Women Engineers and Scientist, University of Warwick on March 10, 2020 by xi'an
ISBA2020 program
Posted in Kids, Statistics, Travel, University life with tags approximate Bayesian inference, Bayesian computing, Bayesian statistics, China, conference, coronavirus epidemcs, high dimensions, ISBA 2020, Kunming, nCo-2019, program, variational Bayes methods, Yunnan on January 29, 2020 by xi'an
The scheduled program for ISBA 2020 is now on-line. And full of exciting sessions, many with computational focus. With dear hopes that the nCo-2019 epidemics will have abated by then (and not solely for the sake of the conference, most obviously!). While early registration ends by 15 April, the deadline for junior travel support ends up this month. And so does the deadline for contributions.
off to BayesComp 20, Gainesville
Posted in pictures, Statistics, Travel, University life with tags BayesComp 2020, Bayesian computation, Bayesian Computing Section, conference, Florida, Gainesville, ISBA, machine learning, MCMC, Spanish moss, tree, University of Florida, USA on January 7, 2020 by xi'an
BayesComp 2020 at a glance
Posted in Statistics, Travel, University life with tags ABC, BayesComp 2020, Bayesian computation, Bayesian nonparametrics, conference, Gainesville, Gaussian processes, HMC, ISBA, MCMC, non-reversible diffusion, poster session, reversible Markov chain, simulation, University of Florida, USA, Wasserstein distance on December 18, 2019 by xi'an
no dichotomy between efficiency and interpretability
Posted in Books, Statistics, Travel, University life with tags AABI, black box, British Columbia, Canada, conference, credit scoring, data challenge, Harvard Data Science Review, HDSR, machine learning, NeurIPS 2019, overfitting, Vancouver 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).
