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Date: Thu 19 Sep 2024

Abstract

Most algorithms for optimising nonlinear functions rely on access to (possibly stochastic) derivative information. However, for problems including adversarial example generation for neural networks and fine-tuning large language models, good derivative information can be difficult to obtain and "derivative-free" optimisation (DFO) algorithms are beneficial. Although there are many approaches for DFO, they generally struggle to solve large-scale problems such as those arising in machine learning. In this talk, I will introduce new scalable DFO algorithms based on random subspaces and develop a novel average-case analysis of such algorithms.