Machine Learning Operations
Bayesian Optimization
Bayesian Optimization is a probabilistic model-based optimization technique used for finding the global optimum of objective functions that are expensive to evaluate, non-convex, and lack an analytical expression.
How It Works
- Surrogate Model – Uses a Gaussian Process to approximate the objective function, providing both mean and uncertainty estimates from observed data.
- Acquisition Function – Balances exploration and exploitation through methods like Expected Improvement (EI), Probability of Improvement (PI), and Upper Confidence Bound (UCB).
- Iterative Process – Alternates between selecting evaluation points, updating the surrogate model, and repeating until stopping criteria are met.
Key Advantages
- Sample efficiency with fewer evaluations
- Handles noisy, discontinuous, black-box objectives
- Provides uncertainty quantification for informed decisions
- Achieves global optimization despite local minima
Primary Applications
- Hyperparameter tuning (neural networks, SVMs, random forests)
- Experiment design in science and engineering
- A/B testing and portfolio optimization
- Robotics control, neural architecture search
- Reinforcement learning and drug discovery
FAQ
Bayesian Optimization is a probabilistic, model-based method for finding the global optimum of expensive, non-convex, black-box functions. Use it when each evaluation (e.g., a simulation run or model training) is costly in time or resources and you want sample-efficient improvement rather than brute force.