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📈 Approximation Theory in Hyperparameter Optimization

Name: Approximation Theory in Hyperparameter Optimization
Author: MySimulator

A 3D surrogate-model lab: watch a kernel-regression surface approximate a hidden validation-loss landscape from sampled hyperparameter trials, with live approximation-error and uncertainty overlays.

AI & Machine Learning3DAdvanced60 FPS

A translucent wireframe hides the true validation-loss landscape over two hyperparameters, while a solid, colored surrogate surface — fitted by kernel regression from a handful of sampled trials — tries to approximate it.

🔬 What It Demonstrates

Kernel-weighted regression is a textbook function-approximation method: nearby evidence dominates, distant evidence fades. Switching to the error and uncertainty views shows exactly where the approximation is trustworthy and where it is guessing.

🎮 How to Use

Add more trials to sharpen the fit, widen or narrow the kernel bandwidth to trade smoothness for detail, and add observation noise to see the surrogate wobble. Switch the surface view to inspect error, uncertainty, or the hidden ground truth.

💡 Did You Know?

Real Bayesian hyperparameter tuners choose their next trial by maximizing an acquisition function built from exactly this kind of surrogate-plus-uncertainty pair — favoring points where the approximation error bound is largest.

⚙ Under the hood

A 3D surrogate-model lab: watch a kernel-regression surface approximate a hidden validation-loss landscape from sampled hyperparameter trials, with live approximation-error and uncertainty overlays.

machine learningoptimizationhyperparametersurrogate modelregressionapproximationThree.js

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