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Evan Markou

2 accepted papers

2025

Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings

NeurIPS 2025spotlight

Many high-dimensional optimisation problems exhibit rich geometric structures in their set of minimisers, often forming smooth manifolds due to over-parametrisation or symmetries. When this structure is known, at least locally, it can be exploited through reduction mappings that reparametrise part o…

Cited by 0SourceScholar
2024

Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame

NeurIPS 2024poster

Neural Collapse (NC) is a recently observed phenomenon in neural networks that characterises the solution space of the final classifier layer when trained until zero training loss. Specifically, NC suggests that the final classifier layer converges to a Simplex Equiangular Tight Frame (ETF), which m…