Data completion and stochastic algorithms for PDE inversion problems with many measurements. - Farbod Roosta-Khorasani; Kees van den Doel; Uri M. Ascher (CoRR, 2013)

Algorithms that satisfy a stopping criterion, probably. - Uri M. Ascher; Farbod Roosta-Khorasani (CoRR, 2014)

Stochastic Algorithms for Inverse Problems Involving PDEs and many Measurements. - Farbod Roosta-Khorasani; Kees van den Doel; Uri M. Ascher (SIAM J. Sci. Comput., 2014)

Improved Bounds on Sample Size for Implicit Matrix Trace Estimators. - Farbod Roosta-Khorasani; Uri M. Ascher (Found. Comput. Math., 2015)

Assessing Stochastic Algorithms for Large Scale Nonlinear Least Squares Problems Using Extremal Probabilities of Linear Combinations of Gamma Random Variables. - Farbod Roosta-Khorasani; Gábor J. Székely; Uri M. Ascher (SIAM/ASA J. Uncertain. Quantification, 2015)

Sub-sampled Newton Methods with Non-uniform Sampling. - Peng Xu; Jiyan Yang; Farbod Roosta-Khorasani; Christopher Ré; Michael W. Mahoney (NIPS, 2016)

FLAG: Fast Linearly-Coupled Adaptive Gradient Method. - Xiang Cheng 006; Farbod Roosta-Khorasani; Peter L. Bartlett; Michael W. Mahoney (CoRR, 2016)

Sub-Sampled Newton Methods I: Globally Convergent Algorithms. - Farbod Roosta-Khorasani; Michael W. Mahoney (CoRR, 2016)

Sub-Sampled Newton Methods II: Local Convergence Rates. - Farbod Roosta-Khorasani; Michael W. Mahoney (CoRR, 2016)

Parallel Local Graph Clustering. - Julian Shun; Farbod Roosta-Khorasani; Kimon Fountoulakis; Michael W. Mahoney (Proc. VLDB Endow., 2016)

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. - Kristofer E. Bouchard; Alejandro F. Bujan; Farbod Roosta-Khorasani; Shashanka Ubaru; Prabhat; Antoine Snijders; Jian-Hua Mao; Edward F. Chang; Michael W. Mahoney; Sharmodeep Bhattacharyya (NIPS, 2017)

FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. - Xiang Cheng 006; Fred (Farbod) Roosta; Stefan Palombo; Peter L. Bartlett; Michael W. Mahoney (AISTATS, 2018)

Out-of-sample extension of graph adjacency spectral embedding. - Keith D. Levin; Farbod Roosta-Khorasani; Michael W. Mahoney; Carey E. Priebe (ICML, 2018)

Invariance of Weight Distributions in Rectified MLPs. - Russell Tsuchida; Farbod Roosta-Khorasani; Marcus Gallagher (ICML, 2018)

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. - Shusen Wang; Farbod Roosta-Khorasani; Peng Xu; Michael W. Mahoney (NeurIPS, 2018)

GPU Accelerated Sub-Sampled Newton textsf{'}s Method. - Sudhir B. Kylasa; Farbod Roosta-Khorasani; Michael W. Mahoney; Ananth Grama (CoRR, 2018)

Distributed Second-order Convex Optimization. - Chih-Hao Fang; Sudhir B. Kylasa; Farbod Roosta-Khorasani; Michael W. Mahoney; Ananth Grama (CoRR, 2018)

Newton-MR: Newton's Method Without Smoothness or Convexity. - Fred (Farbod) Roosta; Yang Liu 310; Peng Xu; Michael W. Mahoney (CoRR, 2018)

Exchangeability and Kernel Invariance in Trained MLPs. - Russell Tsuchida; Fred (Farbod) Roosta; Marcus Gallagher (IJCAI, 2019)

DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. - Rixon Crane; Fred Roosta (NeurIPS, 2019)

GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. - Sudhir B. Kylasa; Fred (Farbod) Roosta; Michael W. Mahoney; Ananth Grama (SDM, 2019)

Richer priors for infinitely wide multi-layer perceptrons. - Russell Tsuchida; Fred Roosta; Marcus Gallagher (CoRR, 2019)

Variational perspective on local graph clustering. - Kimon Fountoulakis; Farbod Roosta-Khorasani; Julian Shun; Xiang Cheng 006; Michael W. Mahoney (Math. Program., 2019)

Sub-sampled Newton methods. - Farbod Roosta-Khorasani; Michael W. Mahoney (Math. Program., 2019)

DINO: Distributed Newton-Type Optimization Method. - Rixon Crane; Fred Roosta (ICML, 2020)

Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. - Chih-Hao Fang; Sudhir B. Kylasa; Fred Roosta; Michael W. Mahoney; Ananth Grama (SC, 2020)

Second-order Optimization for Non-convex Machine Learning: an Empirical Study. - Peng Xu; Fred Roosta; Michael W. Mahoney (SDM, 2020)

Stochastic Normalizing Flows. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (CoRR, 2020)

Average-reward model-free reinforcement learning: a systematic review and literature mapping. - Vektor Dewanto; George Dunn; Ali Eshragh; Marcus Gallagher; Fred Roosta (CoRR, 2020)

Newton-type methods for non-convex optimization under inexact Hessian information. - Peng Xu; Fred Roosta; Michael W. Mahoney (Math. Program., 2020)

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. - Russell Tsuchida; Tim Pearce; Christopher van der Heide; Fred Roosta; Marcus Gallagher (AAAI, 2021)

Shadow Manifold Hamiltonian Monte Carlo. - Christopher van der Heide; Fred Roosta; Liam Hodgkinson; Dirk P. Kroese (AISTATS, 2021)

Non-PSD matrix sketching with applications to regression and optimization. - Zhili Feng; Fred Roosta; David P. Woodruff (UAI, 2021)

Stochastic continuous normalizing flows: training SDEs as ODEs. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (UAI, 2021)

Implicit Langevin Algorithms for Sampling From Log-concave Densities. - Liam Hodgkinson; Robert Salomone; Fred Roosta (J. Mach. Learn. Res., 2021)

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. - Keith D. Levin; Fred Roosta; Minh Tang; Michael W. Mahoney; Carey E. Priebe (J. Mach. Learn. Res., 2021)

Convergence of Newton-MR under Inexact Hessian Information. - Yang Liu 310; Fred Roosta (SIAM J. Optim., 2021)

Inexact Nonconvex Newton-Type Methods. - Zhewei Yao; Peng Xu; Fred Roosta; Michael W. Mahoney (INFORMS J. Optim., 2021)

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 (IGARSS, 2022)

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. - Fred Roosta; Yang Liu 310; Peng Xu; Michael W. Mahoney (EURO J. Comput. Optim., 2022)

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. - Ali Eshragh; Fred Roosta; Asef Nazari; Michael W. Mahoney (J. Mach. Learn. Res., 2022)

MINRES: From Negative Curvature Detection to Monotonicity Properties. - Yang Liu 310; Fred Roosta (SIAM J. Optim., 2022)

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (ICML, 2023)

The Interpolating Information Criterion for Overparameterized Models. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information. - Alexander Lim; Fred Roosta (CoRR, 2023)

Obtaining Pseudo-inverse Solutions With MINRES. - Yang Liu 310; Andre Milzarek; Fred Roosta (CoRR, 2023)

A PAC-Bayesian Perspective on the Interpolating Information Criterion. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints. - Oscar Smee; Fred Roosta (ICML, 2024)

Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. - Eslam Zaher; Maciej Trzaskowski; Quan Nguyen; Fred Roosta (ICML, 2024)

SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. - Ali Eshragh; Luke Yerbury; Asef Nazari; Fred Roosta; Michael W. Mahoney (CoRR, 2024)

Conjugate Direction Methods Under Inconsistent Systems. - Alexander Lim; Yang Liu 310; Fred Roosta (CoRR, 2024)

Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems. - Hossein Askari; Fred Roosta; Hongfu Sun (CoRR, 2024)

Non-Uniform Smoothness for Gradient Descent. - Albert S. Berahas; Lindon Roberts; Fred Roosta (Trans. Mach. Learn. Res., 2024)

Training-free Medical Image Inverses via Bi-level Guided Diffusion Models. - Hossein Askari; Fred Roosta; Hongfu Sun (WACV, 2025)

Obtaining Pseudoinverse Solutions with MINRES. - Yang Liu 310; Andre Milzarek; Fred Roosta (SIAM J. Matrix Anal. Appl., 2025)

Uncertainty Quantification with the Empirical Neural Tangent Kernel. - Joseph Wilson; Chris van der Heide; Liam Hodgkinson; Fred Roosta (CoRR, 2025)

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent. - Oscar Smee; Fred Roosta; Stephen J. Wright 001 (CoRR, 2025)

Determinant Estimation under Memory Constraints and Neural Scaling Laws. - Siavash Ameli; Chris van der Heide; Liam Hodgkinson; Fred Roosta; Michael W. Mahoney (CoRR, 2025)

Importance Sampling for Nonlinear Models. - Prakash Palanivelu Rajmohan; Fred Roosta (CoRR, 2025)

Faithful-Newton Framework: Bridging Inner and Outer Solvers for Enhanced Optimization. - Alexander Lim; Fred Roosta (CoRR, 2025)