Time-Aware Latent Space Bayesian Optimization

2026-03-03 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Molecular Design · Depth: Expert, extended

Summary

Time-Aware Latent-space Bayesian Optimization (TALBO) is a novel framework designed to address temporal drift in objective functions within latent-space Bayesian optimization (LSBO) for structured domains like molecular design. Traditional LSBO methods assume fixed objectives, but real-world design campaigns often face evolving preferences or shifting targets. TALBO integrates time into both the Gaussian Process (GP) surrogate model and the learned generative representation, specifically using a GP-prior variational autoencoder (DGBFGP) to align the latent search space as objectives change. The researchers adapted molecular design tasks to include drifting multi-property objectives and introduced new metrics for changing targets. Across these benchmarks, TALBO consistently outperformed existing LSBO baselines, demonstrating robustness to varying drift speeds and design choices, while maintaining competitive performance even with time-invariant objectives. The method was evaluated over 600 BO rounds, with an initial dataset of 100 molecules, and batch sizes of 10 latent points.

Key takeaway

For research scientists working on de novo molecular or protein design where objectives are subject to temporal drift, you should consider implementing Time-Aware Latent-space Bayesian Optimization (TALBO). This approach explicitly models time in both the surrogate and the generative representation, which is crucial for adaptively tracking high-value inputs as design criteria evolve. Your team can expect more robust performance and faster adaptation to shifting priorities compared to traditional static LSBO methods, even when drift rates vary.

Key insights

TALBO adapts latent-space Bayesian optimization to dynamic objectives by integrating time into both the surrogate and generative representation.

Principles

• Objectives in design campaigns often drift over time.
• Latent space geometry must adapt to evolving objectives.
• Dual temporal modeling improves optimization performance.

Method

TALBO couples a spatio-temporal GP surrogate with a conditional generative representation (DGBFGP) guided by time and covariates, aligning the latent search space with the current objective function.

In practice

• Use TALBO for molecular design with evolving preferences.
• Apply time-aware modeling to both surrogate and latent representation.
• Evaluate performance using current-objective best-so-far and cumulative regret.

Topics

• Latent Space Bayesian Optimization
• Time-Varying Objectives
• Gaussian Processes
• Generative Models
• Molecular Design

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.