The features and shortcomings of Bayesian Networks (with CausalNex)
Benchmarking Bayesian Networks (CausalNex) on a synthethic causal inference dataset
Diagnosing validity of causal effects on decision trees
Diagnosing confounding on leaf nodes of decision trees
Solving the mystery of my dog's breed with ML
Using the Stanford Dogs Dataset, deep learning, and explainability through prototypes to infer the unknown breed of my dog
Training models when data doesn't fit in memory
Using Dask and some other tricks so you can train your models under memory constraints
The Bayesian Bootstrap
Faster, smoother version of the bootstrap that yields better results on small data
Calculating counterfactuals with random forests
Another tree-based causal inference model using nearest neighbors on the embedding produced by a Random Forest
Calculating counterfactuals with decision trees
Decision Trees can be decent causal inference models, with a few tweaks
Risk and Uncertainty in Deep Learning
Building a neural network that can estimate aleatoric and epistemic uncertainty at the same time
A Practical Introduction to Randomized Prior Functions
Understanding a state-of-the-art bayesian deep learning method with Keras code
A Causal Look At What Makes a Kaggler Valuable
Using causal inference to determine what titles and skills will make you earn more
Causal inference and treatment effect estimation
Estimating counterfactual outcomes using Generalized Random Forests, ExtraTrees and embeddings
Calibration of probabilities for tree-based models
Calibrating probabilities of Random Forests and Gradient Boosting Machines with no loss of performance with a stacked logistic regression
Supervised dimensionality reduction and clustering at scale with RFs with UMAP
Uncovering relevant structure and visualizing it at scale by partnering Extremely Randomized Trees and UMAP
Risk-aware bandits
Experimenting with risk-aware reward signals, Thompson Sampling, Bayesian UCB, and the MaRaB algorithm
Approximate bayesian inference for bandits
Experimenting with Conjugate Priors, MCMC Sampling, Variational Inference and Bootstrapping to solve a Gaussian Bandit problem
Non-stationary bandits
Solving a Bernoulli Multi-Armed Bandit problem where reward probabilities change over time
Supervised clustering and forest embeddings
Using forests of randomized trees to uncover structure that really matters in messy data
Bootstrapped Neural Networks, RFs, and the Mushroom bandit
Bootstrapped Neural Networks and Random Forests for solving a more realistic contextual bandit problem
Thompson Sampling, GPs, and Bayesian Optimization
Mixing Thompson Sampling and Gaussian Processes to optimize non-convex and non-differentiable objective functions
Thompson Sampling for Contextual bandits
Solving a Contextual bandit problem with Bayesian Logistic Regression and Thompson Sampling
Introduction to Thompson Sampling: the Bernoulli bandit
Introducing Thompson Sampling and comparing it to the Upper Confidence Bound and epsilon-greedy strategies in a simple problem
