regression

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2023-11-30

Neural process regression 2019-12-03 – 2023-11-28

Learning from ranking, learning to predict ranking Learning preferences, ordinal regressions etc 2020-09-16 – 2023-11-22

Machine learning for partial differential equations 2017-05-15 – 2023-11-17

Calibration of probabilistic forecasts Proper scoring rules, skill scores etc 2015-06-16 – 2023-11-15

Feedback system identification, not necessarily linear Learning dynamics from data 2016-08-01 – 2023-11-15

Physics-informed neural networks 2019-10-15 – 2023-10-19

Neural PDE operator learning Especially forward operators. Image-to-image regression, where the images encode a physical process. 2019-10-15 – 2023-10-19

Probabilistic numerics 2023-07-13 – 2023-09-25

Machine learning for climate systems 2020-04-02 – 2023-09-25

Generalised autoregressive processes 2022-01-10 – 2023-08-11

Materials informatics Machine learning in condensed matter physics, chemistry and materials science 2023-08-01 – 2023-08-08

Gaussian process regression And classification. And extensions. 2019-12-03 – 2023-07-28

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2023-06-28

Bayes functional regression 2019-12-03 – 2023-05-25

Non-Gaussian Bayesian functional regression 2019-10-10 – 2023-05-25

Neural learning dynamical systems 2018-08-13 – 2023-05-23

Multi-objective optimisation 2021-07-14 – 2023-05-04

Covariance estimation Esp Gaussian 2014-11-16 – 2023-04-26

Sparse coding with learnable dictionaries 2014-11-17 – 2023-03-02

Machine learning for physical sciences Turbulent mixing at the boundary between disciplines with differing inertia and viscosity 2017-05-15 – 2022-12-07

Deep learning as a dynamical system 2018-08-13 – 2022-10-30

Bayesian sparsity 2019-01-08 – 2022-10-25

Forecasting Vegan haruspicy 2015-06-16 – 2022-10-08

Precision matrix estimation Especially Gaussain 2014-11-16 – 2022-10-04

(Weighted) least squares fits 2016-09-22 – 2022-10-04

Gaussian process inference by partial updates 2020-12-03 – 2022-09-22

Generalised Ornstein-Uhlenbeck processes Markov/AR(1)-like processes 2022-01-10 – 2022-09-21

Posterior Gaussian process samples by updating prior samples Matheron’s other weird trick 2020-12-03 – 2022-09-20

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2022-08-09

Gaussian process regression software And classification. 2019-12-03 – 2022-07-29

Simulating Gaussian processes on a lattice 2022-03-17 – 2022-07-26

Applied psephology 2016-07-12 – 2022-05-25

(Discrete-measure)-valued stochastic processes 2019-10-10 – 2022-05-04

Forecasting with model averaging Mixture of experts, ensembles and time series 2022-05-04

Measure-valued stochastic processes Moving masses 2020-10-16 – 2022-05-03

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models… 2015-06-07 – 2022-04-21

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Simulating Gaussian processes 2022-03-17

Sparse coding Wavelets, matching pursuit, overcomplete dictionaries… 2014-11-17 – 2022-03-07

Learning Gaussian processes which map functions to functions 2020-12-07 – 2022-02-25

Neural nets with basis decomposition layers 2021-03-09 – 2022-02-01

Running neural nets backwards 2022-01-29

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2022-01-26

Feedback system identification, linear 2016-07-27 – 2022-01-21

Categorical random variates 2017-02-20 – 2022-01-12

Convolutional subordinator processes 2021-03-08 – 2021-12-01

Multi-output Gaussian process regression 2020-12-02 – 2021-11-26

t-processes, t-distributions 2021-11-10 – 2021-11-24

Survey modelling Adjusting for the Lizardman constant 2019-08-29 – 2021-10-24

Missing data Imputation, estimation despite etc 2021-10-07

Fractals and self-similarity 2011-11-13 – 2021-09-22

Vector Gaussian processses 2020-12-02 – 2021-08-16

Convolutional stochastic processes Moving averages of noise 2021-03-01 – 2021-08-16

Essays in stochastic processes My PhD thesis with Zdravko I. Botev 2021-08-13

Generalized linear models 2016-03-24 – 2021-08-05

Models for count data 2015-05-14 – 2021-08-03

Multi-task ML 2021-07-14

Gaussian processes 2016-08-07 – 2021-06-23

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

Deep Gaussian process regression 2021-05-13

Dynamical systems via Koopman operators Composition operators, Dynamic Extended Mode decompositions… 2020-10-13 – 2021-04-09

Convolutional Gaussian processes 2021-03-01

Stochastic processes on manifolds 2021-03-01

Observability and sensitivity in learning dynamical systems Parameter identifiability in dynamical models 2020-11-09

Gaussian process quantile regression 2020-09-16

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

Matching and weighting Making the optimal beverage from the fruit life gave you 2020-04-22

Order statistics 2019-02-21 – 2020-03-17

Delays and reverbs for audio processing …ing …ing …ing 2015-11-11 – 2019-10-22

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

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

Marketing psychology 2017-04-27 – 2017-05-29

Dynamical systems 2016-04-26 – 2016-07-27

Count time series models 2015-06-03 – 2015-12-09

High frequency time series estimation 2016-06-12 – 2015-12-02

Sparse regression for inhomogeneous Hawkes processes My MSc thesis with Professors Didier Sornette and Sara van de Geer 2015-04-28 – 2015-05-12