Active Generation - Generative models for black box optimisation
Active generation advances the union of generative modelling and black-box optimisation so that AI systems can design new artefacts — from molecules and materials to robotic components and algorithms — directly from high-level objectives. We combine powerful generative priors (transformers, flow matching etc.) with machine-learning optimisation loops that decide which experiments to run next, allowing the model to continually refine both its predictive beliefs and its search distribution. This fusion turns design problems that once relied on trial-and-error into targeted, data-driven discovery pipelines, yielding scalable tools for any domain where evaluating a candidate is expensive but generating hypotheses is cheap.
In Variational Search Distributions (VSD) we apply variational inference to the problem of active generation, and introduce a flexible framework for designing sequences such as proteins, with formal guarantees. Software for VSD can be found here. We extend this work for multi-objective optimisation problems in Amortized Active Generation of Pareto Sets, and then to reward model free settings in Generative Bayesian Optimization: Generative Models as Acquisition Functions. We have also applied these methods to actual protein engineering tasks.
We also investigate the spectral properties of sequence (protein, DNA) lansdcapes in Protein fitness landscape: spectral graph theory perspective. Using our theoretical framework we present propagational convolutional neural networks (PCNN), for which we derive theoretical guarantees on the generalization and convergence properties for protein property prediction.
