| 2025 | ICML | Determinant Estimation under Memory Constraints and Neural Scaling Laws. | Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney |
| 2025 | ICML | Importance Sampling for Nonlinear Models. | Prakash Palanivelu Rajmohan, Fred Roosta |
| 2025 | WACV | Training-free Medical Image Inverses via Bi-level Guided Diffusion Models. | Hossein Askari, Fred Roosta, Hongfu Sun |
| 2024 | ICML | Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints. | Oscar Smee, Fred Roosta |
| 2024 | ICML | Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. | Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta |
| 2023 | ICML | Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2022 | IGARSS | Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia. | Dung Nguyen, Yan Zhao, Yifan Zhang, Anh Ngoc-Lan Huynh, Fred Roosta, Graeme L. Hammer, Scott C. Chapman, Andries B. Potgieter |
| 2021 | AAAI | Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. | Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher |
| 2021 | AISTATS | Shadow Manifold Hamiltonian Monte Carlo. | Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese |
| 2021 | UAI | Non-PSD matrix sketching with applications to regression and optimization. | Zhili Feng, Fred Roosta, David P. Woodruff |
| 2021 | UAI | Stochastic continuous normalizing flows: training SDEs as ODEs. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2020 | ICML | DINO: Distributed Newton-Type Optimization Method. | Rixon Crane, Fred Roosta |
| 2020 | SC | Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. | Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama |
| 2020 | SDM | Second-order Optimization for Non-convex Machine Learning: an Empirical Study. | Peng Xu, Fred Roosta, Michael W. Mahoney |