# 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