Stuff that I am currently actively reading. If you are looking at this, and you aren’t me, you should really be re-evaluating your hobbies.

See also my more aspirational paper reading list.

## Currently writing

Not all published yet.

- Anthropic principles in general
- history of the edge of chaos
- You can’t talk about us without us
- Memetics (too big, will never finish)
- X is Yer than Z
- subculture dynamics
- Invasive arguments
- Movement design
~~Table stakes versus tokenism~~- Ethical consumption
- Opinion dynamics (memetics for beginners)
- Scientific community
- But what can I do?
- Decision rules
- interaction effects
- experimental ethics and surveillance
- Myths
- Haunting
~~Something about the fungibility of hipness and cash~~- Speech standards
- Black swan farming
- Where to deploy taboo
- Doing complicated things naively
- Conspiracies as simulations
- cradlesnatch calculator
- Something about the limits of legible fairness versus metis in common property regimes
- Strategic ignorance
- emancipating my tribe
- Institutions for angels
- Lived experience in hypothesis testing
- Beliefs and rituals of tribes, optimisation thereof for out moral wetware
- diversity language
- iterative game theory of communication styles
- Iterative game theory under bounded rationality
- The uncanny ally
- 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

## 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

## 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

## 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

## 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

## 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

Nguyen, Long, and Andy Malinsky. 2020. “Exploration and Implementation of Neural Ordinary Diﬀerential Equations,” 34.

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