Minimum description legnth 2020-08-06

Model complexity penalties Information criteria, degrees of freedom etc 2015-04-22 – 2020-06-22

Long memory time series 0001-01-01 – 2020-05-28

Forecasting Haruspicy 2.0 2015-06-16 – 2020-05-21

Measure concentration inequalities On being 80% sure you are only 20% wrong 2014-11-25 – 2020-05-06

Independence, conditional, statistical 2016-04-21 – 2020-03-28

Model averaging On keeping many incorrect hypotheses and using them all as one goodish one 2017-06-20 – 2020-03-22

Cross validation 2016-09-05 – 2020-03-13

Effective sample size 2016-11-21 – 2020-03-03

Convergence of random variables 2019-12-03

Sparse regression 2016-06-23 – 2019-10-24

Statistical learning theory for time series 2016-11-03 – 2019-10-01

Bayesian model selection 2017-08-20 – 2019-07-22

Optimisation of model functions for experiment design Surrogate optimisation methods for optimising optimisations 2017-04-11 – 2019-07-18

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14

Nearly sufficient statistics How about “Sufficient sufficiency”? – is that taken? 2018-03-13 – 2019-01-14

Bayesian sparsity 2019-01-08

Multiple testing 2015-04-22 – 2018-11-05

AutoML hyperparameter selection with the use of yet more hyperparameters 2017-07-17 – 2017-08-30

Post-selection inference Adaptive data analysis without cheating 2017-08-20

Model/hyperparameter selection 2016-04-15 – 2017-08-20

Generalisation in neural networks Generalisation for street fighters 2017-02-12 – 2017-06-20

Compressed sensing / compressed sampling The fanciest ways of counting zero 2014-08-18 – 2017-06-14

Stability (in learning) 2016-05-25 – 2016-10-05

Penalised/regularised regression 2016-06-23 – 2016-09-15

Sparse model selection 2016-09-05 – 2016-09-08

Statistical learning theory Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc 2016-07-06 – 2016-08-16