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📊 Information Theory in Hyperparameter Optimization

Explore information theory in hyperparameter optimization. Learn about entropy, mutual information, and information-theoretic acquisition functions.

AI & Machine Learning3DAdvanced60 FPS

A purple-toned 3D entropy surface over a two-dimensional hyperparameter search space (learning rate × regularization), where height and color show how uncertain and information-rich each untested configuration is to an information-theoretic Bayesian optimizer.

🔬 What It Demonstrates

Each click evaluates a hidden validation-accuracy landscape at a hyperparameter pair and updates a kernel-smoothed surrogate's predictive entropy H(x) everywhere. Switching to the information-gain field shows the entropy-search-style acquisition score that trades exploration against exploitation.

🎮 How to Use

Click anywhere on the surface to run a trial there. Adjust the kernel lengthscale and observation noise to see how fast uncertainty collapses, toggle between entropy and information-gain coloring, and use "Suggest next trial" to auto-pick the highest-information point.

💡 Did You Know?

Entropy Search and Max-value Entropy Search choose hyperparameter trials by maximizing expected reduction in the entropy of the optimizer's belief about where the true optimum lies — often beating plain expected-improvement search in trial efficiency.

⚙ Under the hood

Explore information theory in hyperparameter optimization. Learn about entropy, mutual information, and information-theoretic acquisition functions.

machine learningoptimizationinformation theoryhyperparametersentropyalgorithmsThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install