Differentiable model selection Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on 2020-09-25 – 2021-04-13
Optimal transport inference I feel the earth mover under my feet, I feel the ψ tumbling down, I feel my heart start to trembling, Whenever you're around my empirical density in minimal transport cost 2021-03-16
Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2021-03-03
Hyperparameter optimization in ML Replacing a hyperparameter problem with a hyperhyperparameter problem which feels like progress I guess 2020-09-25 – 2021-03-01
Jax 2020-09-15 – 2021-02-18
AutoML 2017-07-17 – 2020-10-02
Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11
Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23
Limit Theorems Asymptotic distributions of random processes 2014-11-25 – 2020-05-06
Restricted isometry properties Plus incoherence, irrepresentability, and other uncertainty bounds for a sparse world, and maybe frame theory, what’s that now? 2017-06-12 – 2020-03-09
Random change of time Stochastic processes derived by varying the rate of time’s passage, which is more convenient than I imagined 2015-08-05 – 2020-02-10
Inner product spaces The most highly developed theory of squaring things 2019-01-01 – 2019-02-11
Linear algebra If the thing is twice as big, the transformed version of the thing is also twice as big. {End} 2011-04-06 – 2018-08-07
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