machine_learning

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-01-26

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models… 2015-06-07 – 2022-01-25

Hydrology models Rivers, aquifers and other wet things that can flood your house 2020-09-07 – 2022-01-24

Multi fidelity models Data-driven coarse graining 2020-08-24 – 2022-01-20

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-01-13

Measure-valued stochastic processes Including completely random measures and many generalizations 2020-10-16 – 2022-01-10

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2021-12-15

Causality via potential outcomes Neyman-Rubin, counterfactuals, conditional treatment effects, instrumental variables and related tricks 2016-10-26 – 2021-12-10

Causal inference in highly parameterized ML 2020-09-18 – 2021-12-10

Message-passing algorithms in graphical models 2014-11-25 – 2021-12-08

Overparameterization a.k.a. improper learning 2018-04-04 – 2021-12-08

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

Ensembling neural nets Monte Carlo 2020-12-14 – 2021-11-25

Visualising probabilistic graphical models Also related models, such as Neural nets 2018-03-29 – 2021-11-24

Convolutional neural networks 2017-11-10 – 2021-11-21

Technological singularities Incorporating hard AI take-offs, game-over high scores, the technium, deus-ex-machina, deus-ex-nube, nerd raptures and so forth 2016-12-01 – 2021-11-18

Neural nets for “implicit representations” 2021-01-21 – 2021-11-16

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2021-11-03

External validity Transfer learning, dataset shift, learning under covariate shift, transferable learning, domain adaptation etc 2020-10-17 – 2021-11-03

Differentiable PDE solvers Method of adjoints etc 2017-05-15 – 2021-10-28

Machine learning for partial differential equations 2017-05-15 – 2021-10-28

Interaction effects are probably what we want to estimate 2022-01-25 – 2021-10-28

Random graphical models Causality in amongst confusion 2021-10-26 – 2021-10-28

Graph neural nets 2020-09-16 – 2021-10-27

Probably Approximately Correct 2014-11-24 – 2021-10-27

Neural music synthesis 2016-01-15 – 2021-10-14

Random neural networks 2017-02-17 – 2021-10-12

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

Neural nets for “implicit representations” 2021-01-21 – 2021-10-05

Neural net attention mechanisms On brilliance through selective ignorance 2017-12-20 – 2021-10-01

Brains Neural networks made of real neurons, in functioning brains 2014-11-03 – 2021-09-26

Regularising neural networks Generalisation for street fighters 2017-02-12 – 2021-09-24

Machine learning for physical sciences Turbulent mixing at the boundary between two disciplines with differing inertia and viscosity 2017-05-15 – 2021-09-20

Economics of automation When to the robots come for my job? 2021-09-20

Implementing neural nets 2016-10-14 – 2021-09-20

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

Biological basis of language 2018-01-11 – 2021-09-16

Natural language processing Automatic processing of labguage 2018-01-11 – 2021-09-16

Voice transcriptions and speech recognition 2019-01-08 – 2021-09-13

Neural nets with implicit layers Also, declarative networks 2020-12-08 – 2021-09-07

Recurrent neural networks 2016-06-16 – 2021-09-06

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

Grammar induction 2011-05-30 – 2021-08-09

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

Neural network activation functions 2017-01-12 – 2021-08-02

Here’s how I would do art with machine learning if I had to 2016-06-06 – 2021-07-26

Learning summary statistics 2020-04-22 – 2021-07-15

Multi-task ML 2021-07-14

ML on small devices Putting intelligence on chips small enough to be in disconcerting places 2016-10-14 – 2021-07-13

Moral philosophy 2014-08-04 – 2021-07-01

Distributed sensing, swarm sensing, adaptive social learning 2014-10-13 – 2021-06-28

ML Koans Passing through the NAND-gate 2021-06-23

Voice fakes 2018-09-06 – 2021-06-17

Neural net kernels 2019-09-16 – 2021-05-24

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

Compressing neural nets pruning, compacting and otherwise fitting a good estimate into fewer parameters 2016-10-14 – 2021-05-07

ML benchmarks and their pitfalls On marginal efficiency gain in paperclip manufacture 2020-08-16 – 2021-04-13

Statistics and machine learning 2011-04-15 – 2021-04-08

Model interpretation and explanation Colorizing black boxes 2016-09-01 – 2021-03-15

Statistics, computational complexity thereof 2020-11-03 – 2021-03-10

Neural nets with basis decomposition layers 2021-03-09

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2021-03-03

Mind reading by computer The ultimate inverse problem 2017-07-01 – 2021-03-03

Memory in machine learning 2021-03-03 – 2021-03-03

Convolutional Gaussian processes 2021-03-01

Stochastic processes on manifolds 2021-03-01

Causal inference in the continuous limit 2021-02-17

Auditory features descriptors, maps, representations for audio 2019-11-13 – 2021-01-14

Statistical mechanics of statistics 2016-12-01 – 2021-01-06

Why does deep learning work? Are we in the pocket of Big VRAM? 2017-05-30 – 2020-12-14

Nonparametrically learning dynamical systems 2018-08-13 – 2020-12-08

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

Audiovisuals Synesthetic and other cross-media audio stunts 2020-10-26

Natural language processing software Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. 2018-01-11 – 2020-10-01

Big data ML best practice 2020-09-16 – 2020-09-21

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

Neural nets designing the fanciest usable differentiable loss surface 2016-10-14 – 2020-09-09

Causal graphical model reading group 2020 An introduction to conditional independence DAGs and their use for causal inference. 2020-08-30 – 2020-09-03

Causal Bayesian networks Staged tree models, probability trees …Causalan Bayesian networks 2020-11-01 – 2020-09-01

Emulators and surrogate models Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

Model fairness 2018-11-29 – 2020-08-21

Bushfire models 2020-09-07 – 2020-08-19

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Deep fakery 2020-06-15

Directed graphical models 2017-09-20 – 2020-05-13

AV controller interfaces 2019-12-10 – 2020-05-11

Likelihood free inference 2020-04-22

Learning graphical models from data What is independent of what? 2017-09-20 – 2020-04-11

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

Deep learning as a dynamical system 2018-08-13 – 2020-04-02

Nonparametrically learning spatiotemporal systems 2020-09-16 – 2020-04-02

Teaching computers to write music 2016-06-06 – 2020-03-25

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Learning in adaptive systems On staring into scopophilic abysses 2019-10-19 – 2020-01-22

Factor graphs 2019-12-16

Controllerism Making thing happen by waving your arms about on stage 2014-11-17 – 2019-12-10

Audio source separation 2019-11-04 – 2019-11-26

Tunings 2015-10-29 – 2019-11-18

Machine listening Statistical models for audio 2014-10-10 – 2019-11-12

Javascript machine learning 2017-01-13 – 2019-11-09

ISMIR 2019 Music Nerds in Delft 2019-11-04 – 2019-11-09

Inference on graphical models Given what I know about what I know, what do I know? 2017-09-20 – 2019-10-28

Undirected graphical models 2017-09-20 – 2019-10-28

Bio computing 2016-05-29 – 2019-10-14

Probabilistic graphical models over continuous index sets 2014-08-05 – 2019-09-25

Automatic programming 2016-10-14 – 2019-09-11

Differentiable learning of automata 2016-10-14 – 2019-09-11

Machine vision 2015-01-03 – 2018-11-14

Gesture recognition 2014-10-17 – 2018-11-12

Moral calculus 2014-08-04 – 2017-11-21

Probabilistic graphical models Cleaving reality at the joint 2014-08-05 – 2017-09-11

Quantum-probabilistic graphical models 2017-08-07

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

Entity embeddings 2017-04-01

Musical metrics and manifolds 2014-09-26 – 2017-03-27

Genetic algorithms 2011-04-06 – 2016-12-28

Greatest hits 2016-11-19 – 2016-12-12

UNSW Stats reading group 2016 - Causal DAGs An introduction to conditional independence DAGs and their use for causal data. 2016-10-17 – 2016-10-21

Inference from disorder 2016-10-19

Indirect inference 2014-12-23 – 2015-12-15

Pattern machine 2011-06-27 – 2015-11-24