Entropic determinants of massive matrices. - Diego Granziol; Stephen J. Roberts (IEEE BigData, 2017)

Entropic Trace Estimates for Log Determinants. - Jack K. Fitzsimons; Diego Granziol; Kurt Cutajar; Michael A. Osborne; Maurizio Filippone; Stephen J. Roberts (ECML/PKDD (1), 2017)

Fast Information-theoretic Bayesian Optimisation. - Bin Xin Ru; Mark McLeod; Diego Granziol; Michael A. Osborne (ICML, 2018)

VBALD - Variational Bayesian Approximation of Log Determinants. - Diego Granziol; Edward Wagstaff; Bin Xin Ru; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Entropic Spectral Learning in Large Scale Networks. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

A Maximum Entropy approach to Massive Graph Spectra. - Diego Granziol; Robin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2019)

MLRG Deep Curvature. - Diego Granziol; Xingchen Wan; Timur Garipov; Dmitry P. Vetrov; Stephen Roberts 001 (CoRR, 2019)

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (Entropy, 2019)

Iterate Averaging Helps: An Alternative Perspective in Deep Learning. - Diego Granziol; Xingchen Wan; Stephen Roberts 001 (CoRR, 2020)

Beyond Random Matrix Theory for Deep Networks. - Diego Granziol (CoRR, 2020)

Flatness is a False Friend. - Diego Granziol (CoRR, 2020)

Curvature is Key: Sub-Sampled Loss Surfaces and the Implications for Large Batch Training. - Diego Granziol (CoRR, 2020)

Explaining the Adaptive Generalisation Gap. - Diego Granziol; Samuel Albanie; Xingchen Wan; Stephen J. Roberts (CoRR, 2020)

Spectral machine learning. - Diego Granziol (2020)

Applicability of Random Matrix Theory in Deep Learning. - Nicholas P. Baskerville; Diego Granziol; Jonathan P. Keating (CoRR, 2021)

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. - Diego Granziol; Binxin Ru; Xiaowen Dong 001; Stefan Zohren; Michael A. Osborne; Stephen J. Roberts (Algorithms, 2022)

Universal characteristics of deep neural network loss surfaces from random matrix theory. - Nicholas P. Baskerville; Jonathan P. Keating; Francesco Mezzadri; Joseph Najnudel; Diego Granziol (CoRR, 2022)

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training. - Diego Granziol; Stefan Zohren; Stephen Roberts 001 (J. Mach. Learn. Res., 2022)

Iterate Averaging in the Quest for Best Test Error. - Diego Granziol; Nicholas P. Baskerville; Xingchen Wan; Samuel Albanie; Stephen Roberts 001 (J. Mach. Learn. Res., 2024)

Compute-Optimal LLMs Provably Generalize Better with Scale. - Marc Anton Finzi; Sanyam Kapoor; Diego Granziol; Anming Gu; Christopher De Sa; J. Zico Kolter; Andrew Gordon Wilson (ICLR, 2025)

HessFormer: Hessians at Foundation Scale. - Diego Granziol (CoRR, 2025)

A Linear Approach to Data Poisoning. - Diego Granziol; Donald Flynn (CoRR, 2025)