kaggle

My first (and only) Kaggle Top 10%

bandits

A Practical Introduction to Randomized Prior Functions

Risk-aware bandits

Approximate bayesian inference for bandits

Non-stationary bandits

Bootstrapped Neural Networks, RFs, and the Mushroom bandit

Thompson Sampling for Contextual bandits

Introduction to Thompson Sampling: the Bernoulli bandit

bayesian

Risk and Uncertainty in Deep Learning

A Practical Introduction to Randomized Prior Functions

Risk-aware bandits

Approximate bayesian inference for bandits

Non-stationary bandits

Bootstrapped Neural Networks, RFs, and the Mushroom bandit

Thompson Sampling, GPs, and Bayesian Optimization

Thompson Sampling for Contextual bandits

Introduction to Thompson Sampling: the Bernoulli bandit

clustering

Diagnosing validity of causal effects on decision trees

The Bayesian Bootstrap

Calculating counterfactuals with random forests

Calculating counterfactuals with decision trees

A Causal Look At What Makes a Kaggler Valuable

Causal inference and treatment effect estimation

Supervised dimensionality reduction and clustering at scale with RFs with UMAP

Supervised clustering and forest embeddings

dreduction

Solving the mystery of my dog's breed with ML

The Bayesian Bootstrap

Calculating counterfactuals with random forests

Calculating counterfactuals with decision trees

A Causal Look At What Makes a Kaggler Valuable

Causal inference and treatment effect estimation

Supervised dimensionality reduction and clustering at scale with RFs with UMAP

Supervised clustering and forest embeddings

classification

Solving the mystery of my dog's breed with ML

Training models when data doesn't fit in memory

Calibration of probabilities for tree-based models

causal inference

The features and shortcomings of Bayesian Networks (with CausalNex)

Diagnosing validity of causal effects on decision trees

The Bayesian Bootstrap

Calculating counterfactuals with random forests

Calculating counterfactuals with decision trees

A Causal Look At What Makes a Kaggler Valuable

Causal inference and treatment effect estimation

regression

Risk and Uncertainty in Deep Learning

A Practical Introduction to Randomized Prior Functions

engineering

Training models when data doesn't fit in memory