# 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