Probably actually reading/writing

Currently writing

Not all published yet.

1. Anthropic principles in general
2. history of the edge of chaos
3. You can’t talk about us without us
4. Memetics (too big, will never finish)
5. X is Yer than Z
6. subculture dynamics
7. Invasive arguments
8. Movement design
9. Table stakes versus tokenism
10. Ethical consumption
11. Opinion dynamics (memetics for beginners)
12. Scientific community
13. But what can I do?
14. Decision rules
15. interaction effects
16. experimental ethics and surveillance
17. Myths
18. Haunting
19. Something about the fungibility of hipness and cash
20. Speech standards
21. Black swan farming
22. Where to deploy taboo
23. Doing complicated things naively
24. Conspiracies as simulations
25. cradlesnatch calculator
26. Something about the limits of legible fairness versus metis in common property regimes
27. Strategic ignorance
28. emancipating my tribe
29. Institutions for angels
30. Lived experience in hypothesis testing
31. Beliefs and rituals of tribes, optimisation thereof for out moral wetware
32. diversity language
33. iterative game theory of communication styles
34. Iterative game theory under bounded rationality
35. The uncanny ally
36. Elliptical belief propagation

Neurips 2021

• Storchastic: A Framework for General Stochastic Automatic Differentiation
• Causal Inference & Machine Learning: Why now?
• Physical Reasoning and Inductive Biases for the Real World
• Real-Time Optimization for Fast and Complex Control Systems
• [2104.13478] Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

gd

• The zen of gradient descent
• Optimization I | Simons Institute for the Theory of Computing
• Computer Scientists Discover Limits of Major Research Algorithm

Music

Nestup / cutelabnyc/nested-tuplets: Fancy javascript for manipulating nested tuplets.

Foundations

• Probability Theory (For Scientists and Engineers)
• Course Notes 7: Gaussian Process Engineering | Michael Betancourt on Patreon
• Conditional Probability Theory (For Scientists and Engineers)
• conditional_probability.pdf
• Autodiff for Implicit Functions Paper Live Stream Wed 1/12 at 11 AM EST | Michael Betancourt on Patreon
• New Autodiff Paper | Michael Betancourt on Patreon
• Rumble in the Ensemble
• Scholastic Differential Equations | Michael Betancourt on Patreon
• Identity Crisis
• Invited Talk - Michael Bronstein
• Product Placement
• (Not So) Free Samples
• Sampling Case Study Live Stream Wed 4/14 at 2 PM EDT
• Updated Geometric Optimization Paper
• We Built Sparse City

nonparametrics

• Tamara Broderick 1: Bayesian Nonparametrics
• Tamara Broderick 2: Bayesian Nonparametrics
• Tamara Broderick 3: Bayesian Nonparametrics

radiotelescopy

• Bayesian Regression Using NumPyro — NumPyro documentation
• Example: Bayesian Neural Network with SteinVI — NumPyro documentation
• Example: Deep Markov Model inferred using SteinVI — NumPyro documentation
• Example: Hidden Markov Model — NumPyro documentation
• Example: AR2 process — NumPyro documentation
• Stochastic Variational Inference (SVI) — NumPyro documentation
• t.dvi - minka-dirichlet.pdf
• Example: Sequential Monte Carlo Filtering — Pyro Tutorials 1.8.1 documentation
• time_series_forecasting.ipynb - Colaboratory
• 02whole.pdf
• Optimal structure and parameter learning of Ising models - PMC
• Machine Learning Trick of the Day (6): Tricks with Sticks ← The Spectator

Generalising GaBP

• Entropy | Free Full-Text | Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation | HTML

GP research

• https://www.patreon.com/posts/new-linearized-69325387
• Deep integro-difference equation models for spatio-temporal forecasting - ScienceDirect
• GP Regression with Grid Structured Training Data — GPyTorch 1.8.1 documentation
• GP Regression with LOVE for Fast Predictive Variances and Sampling — GPyTorch 1.8.0 documentation
• Structured Kernel Interpolation (SKI/KISS-GP) — GPyTorch 1.8.0 documentation
• Probabilistic Graphical Models Lecture 21: Advanced Gaussian Processes - andrewgp2.pdf
• Scalable Bayesian spatial analysis with Gaussian Markov random fields - FULLTEXT01.pdf
• Solving Inverse Problems with NNs — Physics-based Deep Learning
• Murali Haran’s spatial GP
• Bayesian inference with INLA
• R-INLA Project
• Regression-based covariance functions for nonstationary spatial modeling
• kalman-jax/sde_gp.py at master · AaltoML/kalman-jax
• Scaling multi-output Gaussian process models with exact inference
• AaltoML/kalman-jax: Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX

Invenia’s GP expansion ideas

• Gaussian Processes: from one to many outputs
• Implementing a scalable multi-output GP model with exact inference
• Scaling multi-output Gaussian process models with exact inference
• wesselb/stheno: Gaussian process modelling in Python
• Linear Models from a Gaussian Process Point of View with Stheno and JAX

Misc

• How to write a great research paper - Microsoft Research
• Cheng Soon Ong, Marc Peter Deisenroth | There and Back Again: A Tale of Slopes and Expectations
• David Duvenaud, J. Zico Kolter, Matt Johnson | Deep Implicit Layers: Neural ODEs, Equilibrium Models and Beyond
• Encoder Autonomy | Machine Thoughts
• The Notion of “Double Descent” | Mad (Data) Scientist
• Jaan on translating between variational terminology in physics and ML
• Jaan on VAE
• pamamakouros on normalizing flows
• Eric Jang on normalizing flows
• Sander on typicality
• Sander on waveform audio
• yuge shi’s ELBO gradient post is excellent
• Francis Bach, the many faces of integration by parts.
• Efficiently sampling functions from Gaussian process posteriors
• “Sethuraman” rep of Dirichlet proc = stick breaking
• Bubeck on hot results in learning theory takes him far from the world of mirror descent. Also lectures well, IMO.
• Causality for Machine Learning

General emulation

Sensitivity Analysis in Emukit.

SDEs in optimisation

Nguyen and Malinsky (2020)

Statistical Inference via Convex Optimization.

Conjugate functions illustrated.

Francis Bach on the use of geometric sums and a different take by Julyan Arbel.

Tutorial to approximating differentiable control problems. An extension of this is universal differential equations.

References