🎛️ Multi-Objective & Automated Hyperparameter Tuning

About this simulation

The left surface is a synthetic loss landscape over two hyperparameters (e.g. learning rate and regularization strength). An automated tuner drops trial points onto it, searching for the lowest loss. The right panel is a live Pareto front: every trial also has an accuracy and an inference-latency cost, and in the real world you rarely get the best of both — improving one often worsens the other.

How it works

• Random search scatters trials uniformly across the space with no memory of past results.
• Bayesian-style search explores broadly at first, then exploits the best region found so far with a shrinking search radius — visualized as a converging trail.
• Warm start simulates transfer learning: the tuner begins near a region that worked well on a related, previously-tuned task instead of starting from scratch.
• Points that no other trial beats on both accuracy and latency are Pareto-optimal (highlighted, connected by the front curve). Everything else is dominated (dimmed).
• The preference slider performs scalarization — it weights accuracy vs. latency into one score and picks the best trade-off point on the front, the way an AutoML system would when you tell it what you care about.

Controls

• Search strategy — random vs. smart/adaptive sampling.
• Trial budget — how many configurations the tuner is allowed to evaluate.
• Transfer-learning warm start — bias the early search near a known-good prior region.
• Accuracy ⟷ Latency preference — moves the recommended pick along the Pareto front live.
• ▶ Run search — regenerates and replays the trial sequence with current settings.

Did you know?

Real AutoML systems (Optuna, Ray Tune, Vertex AI Vizier) use exactly this explore-then-exploit pattern, often built on Bayesian optimization or population-based training, and report results as a Pareto front so a human can choose the operating point that fits their deployment constraints.