# metrics

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
– 2023-01-16Maximum Mean Discrepancy
2016-08-21
– 2023-01-13(Kernelized) Stein variational gradient descent
KSVD, SVGD
2022-11-02
– 2023-01-09Probability divergences
Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses
2014-11-25
– 2023-01-06Neural codecs
Digital recordings and converting between them.
2020-04-23
– 2022-12-19Density ratio tricks
2022-12-06Randomised linear algebra
2016-08-16
– 2022-10-22Neural tangent kernel
2020-12-09
– 2022-10-14Transcoding
Digital recordings and converting between them.
2020-04-23
– 2022-10-05Precision matrix estimation
Especially Gaussain
2014-11-16
– 2022-10-04Distributional robustness in inference
2019-07-12
– 2022-08-30Informations
Entropies and other measures of surprise
2011-11-25
– 2022-08-30Generalized Bayesian Computation
2019-10-03
– 2022-04-28Inference without KL divergence
2019-10-03
– 2022-04-28Gumbel (soft) max tricks
Concrete distribution, relaxed categorical etc
2017-02-20
– 2022-04-01Change points
Looking for regime changes in stochastic processes. a.k.a. Switching state space models
2021-11-29
– 2022-04-01Detecting stationarity in stochastic processes
Change-points, trends and transients
2021-11-29
– 2022-04-01Random binary vectors
The class of distributions that cause you to reinvent Shannon information if you stare at them long enough
2017-02-20
– 2022-03-30Classification
Computer says no
2017-02-20
– 2022-03-28Variational inference
On fitting something not too far from a pretty good model that is not too hard
2016-03-22
– 2022-02-10Karhunen-Loève expansions
2019-09-16
– 2022-02-01Categorical random variates
2017-02-20
– 2022-01-12Combining kernels
2019-09-16
– 2021-11-25Optimal transport metrics
Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances
2019-05-30
– 2021-06-08Stein’s method
His eyes are like angels but his heart is cold / No need to ask / He’s a Stein operator
2021-03-12
– 2021-06-01Neural net kernels
2019-09-16
– 2021-05-24Randomized low dimensional projections
2021-03-12
– 2021-05-24Infinite width limits of neural networks
2020-12-09
– 2021-05-11Kernel zoo
2019-09-16
– 2021-03-30Generically approximating probability distributions
2021-03-12
– 2021-03-22Measure concentration inequalities
On being 80% sure I am only 20% wrong
2014-11-25
– 2021-03-04Learning covariance functions
Learning a family of covariances at once
2019-09-16
– 2021-03-01Miscellaneous nonstationary kernels
2019-09-16
– 2021-01-21Warping of stationary stochastic processes
2019-09-16
– 2021-01-21Covariance functions
Variograms, Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap
2019-09-16
– 2021-01-05Distribution regression
2020-12-01Independence, conditional, statistical
2016-04-21
– 2020-09-13Variational inference
On fitting the best model one can be bothered to
2016-03-22
– 2020-05-24Empirical estimation of information
Informing yourself from your data how informative your data was
2011-04-19
– 2020-04-28Likelihood free inference
2020-04-22(Reproducing) kernel tricks
2014-08-18
– 2020-01-20Covariance matrix estimation
Esp Gaussian
2014-11-16
– 2019-09-21Representer theorems
2019-09-16Inner product spaces
The most highly developed theory of squaring things
2019-01-01
– 2019-02-11Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Normed spaces
2019-01-01
– 2019-01-04The simplex
2016-10-25Statistical 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