# regression

System identification in continuous time
Learning in continuous ODEs, SDEs and CDEs
2016-08-01
– 2023-11-30Neural process regression
2019-12-03
– 2023-11-28Learning from ranking, learning to predict ranking
Learning preferences, ordinal regressions etc
2020-09-16
– 2023-11-22Machine learning for partial differential equations
2017-05-15
– 2023-11-17Calibration of probabilistic forecasts
Proper scoring rules, skill scores etc
2015-06-16
– 2023-11-15Feedback system identification, not necessarily linear
Learning dynamics from data
2016-08-01
– 2023-11-15Physics-informed neural networks
2019-10-15
– 2023-10-19Neural PDE operator learning
Especially forward operators. Image-to-image regression, where the images encode a physical process.
2019-10-15
– 2023-10-19Probabilistic numerics
2023-07-13
– 2023-09-25Machine learning for climate systems
2020-04-02
– 2023-09-25Generalised autoregressive processes
2022-01-10
– 2023-08-11Materials informatics
Machine learning in condensed matter physics, chemistry and materials science
2023-08-01
– 2023-08-08Gaussian process regression
And classification. And extensions.
2019-12-03
– 2023-07-28Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2023-06-28Bayes functional regression
2019-12-03
– 2023-05-25Non-Gaussian Bayesian functional regression
2019-10-10
– 2023-05-25Neural learning dynamical systems
2018-08-13
– 2023-05-23Multi-objective optimisation
2021-07-14
– 2023-05-04Covariance estimation
Esp Gaussian
2014-11-16
– 2023-04-26Sparse coding with learnable dictionaries
2014-11-17
– 2023-03-02Machine learning for physical sciences
Turbulent mixing at the boundary between disciplines with differing inertia and viscosity
2017-05-15
– 2022-12-07Deep learning as a dynamical system
2018-08-13
– 2022-10-30Bayesian sparsity
2019-01-08
– 2022-10-25Forecasting
Vegan haruspicy
2015-06-16
– 2022-10-08Precision matrix estimation
Especially Gaussain
2014-11-16
– 2022-10-04(Weighted) least squares fits
2016-09-22
– 2022-10-04Gaussian process inference by partial updates
2020-12-03
– 2022-09-22Generalised Ornstein-Uhlenbeck processes
Markov/AR(1)-like processes
2022-01-10
– 2022-09-21Posterior Gaussian process samples by updating prior samples
Matheron’s other weird trick
2020-12-03
– 2022-09-20Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2022-08-09Gaussian process regression software
And classification.
2019-12-03
– 2022-07-29Simulating Gaussian processes on a lattice
2022-03-17
– 2022-07-26Applied psephology
2016-07-12
– 2022-05-25(Discrete-measure)-valued stochastic processes
2019-10-10
– 2022-05-04Forecasting with model averaging
Mixture of experts, ensembles and time series
2022-05-04Measure-valued stochastic processes
Moving masses
2020-10-16
– 2022-05-03Hierarchical models
DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models…
2015-06-07
– 2022-04-21Measure-valued random variates
Including completely random measures and many generalizations
2020-10-16
– 2022-03-30Simulating Gaussian processes
2022-03-17Sparse coding
Wavelets, matching pursuit, overcomplete dictionaries…
2014-11-17
– 2022-03-07Learning Gaussian processes which map functions to functions
2020-12-07
– 2022-02-25Neural nets with basis decomposition layers
2021-03-09
– 2022-02-01Running neural nets backwards
2022-01-29Learning on manifolds
Finding the lowest bit of a krazy straw, from the inside
2011-10-21
– 2022-01-26Feedback system identification, linear
2016-07-27
– 2022-01-21Categorical random variates
2017-02-20
– 2022-01-12Convolutional subordinator processes
2021-03-08
– 2021-12-01Multi-output Gaussian process regression
2020-12-02
– 2021-11-26t-processes, t-distributions
2021-11-10
– 2021-11-24Survey modelling
Adjusting for the Lizardman constant
2019-08-29
– 2021-10-24Missing data
Imputation, estimation despite etc
2021-10-07Fractals and self-similarity
2011-11-13
– 2021-09-22Vector Gaussian processses
2020-12-02
– 2021-08-16Convolutional stochastic processes
Moving averages of noise
2021-03-01
– 2021-08-16Essays in stochastic processes
My PhD thesis with Zdravko I. Botev
2021-08-13Generalized linear models
2016-03-24
– 2021-08-05Models for count data
2015-05-14
– 2021-08-03Multi-task ML
2021-07-14Gaussian processes
2016-08-07
– 2021-06-23Random-forest-like methods
A selection of randomly stopped clocks is never far from wrong.
2015-09-23
– 2021-06-17Deep Gaussian process regression
2021-05-13Dynamical systems via Koopman operators
Composition operators, Dynamic Extended Mode decompositions…
2020-10-13
– 2021-04-09Convolutional Gaussian processes
2021-03-01Stochastic processes on manifolds
2021-03-01Observability and sensitivity in learning dynamical systems
Parameter identifiability in dynamical models
2020-11-09Gaussian process quantile regression
2020-09-16Long memory time series
2011-11-13
– 2020-05-28Matching and weighting
Making the optimal beverage from the fruit life gave you
2020-04-22Order statistics
2019-02-21
– 2020-03-17Delays and reverbs for audio processing
…ing …ing …ing
2015-11-11
– 2019-10-22Statistical learning theory for time series
2016-11-03
– 2019-10-01Wacky regression
2015-09-23
– 2019-05-02Marketing psychology
2017-04-27
– 2017-05-29Dynamical systems
2016-04-26
– 2016-07-27Count time series models
2015-06-03
– 2015-12-09High frequency time series estimation
2016-06-12
– 2015-12-02Sparse regression for inhomogeneous Hawkes processes
My MSc thesis with Professors Didier Sornette and Sara van de Geer
2015-04-28
– 2015-05-12