Model selection

Fractals and self-similarity 2011-11-13 – 2021-09-17

Meta learning Few-shot learning, learning fast weights, learning to learn 2021-09-16

Forecasting Haruspicy 2.0 2015-06-16 – 2021-09-06

Tests, statistical Maybe also design of experiments while we are here? 2014-08-23 – 2021-08-04

Sequential experiments Especially multiple sequential experiments 2021-08-04

Gradient descent at scale 2021-07-14

Random-forest-like methods A selection of randomly stopped clocks is never far from wrong. 2015-09-23 – 2021-06-17

Stein’s method 2021-03-12 – 2021-06-01

Limit Theorems Asymptotic distributions of random processes 2014-11-25 – 2021-05-17

Cross validation 2016-09-05 – 2021-05-13

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

Compressing neural nets 2016-10-14 – 2021-05-07

Differentiable model selection Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on 2020-09-25 – 2021-04-13

Generically approximating probability distributions 2021-03-12 – 2021-03-22

Scaling laws for very large neural nets 2021-01-14 – 2021-03-15

Matrix measure concentration inequalities and bounds 2014-11-25 – 2021-03-08

Measure concentration inequalities On being 80% sure you are only 20% wrong 2014-11-25 – 2021-03-04

Hyperparameter optimization in ML Replacing a hyperparameter problem with a hyperhyperparameter problem which feels like progress I guess 2020-09-25 – 2021-03-01

Regularising neural networks Generalisation for street fighters 2017-02-12 – 2020-12-01

Weighted data in statistics 2020-11-04 – 2020-11-06

Automatic design of experiments Minesweeper++ 2017-04-11 – 2020-10-13

Sparse model selection 2016-09-05 – 2020-10-02

AutoML 2017-07-17 – 2020-10-02

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

Independence, conditional, statistical 2016-04-21 – 2020-09-13

Minimum description length 2020-08-06

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

Long memory time series 2011-11-13 – 2020-05-28

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

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

Wacky regression 2015-09-23 – 2019-05-02

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

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

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

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

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

Randomised linear algebra 2016-08-16

Kernel approximation 2016-07-27