Fred (Farbod) Roosta

According to our database, Fred (Farbod) Roosta authored at least 53 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Non-Uniform Smoothness for Gradient Descent.

Albert S. Berahas, Lindon Roberts, Fred Roosta

Trans. Mach. Learn. Res., 2024

Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems.

Hossein Askari, Fred Roosta, Hongfu Sun

CoRR, 2024

Conjugate Direction Methods Under Inconsistent Systems.

Alexander Lim, Yang Liu, Fred Roosta

CoRR, 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

Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution.

Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints.

Oscar Smee, Fred Roosta

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Obtaining Pseudo-inverse Solutions With MINRES.

Yang Liu, Andre Milzarek, Fred Roosta

CoRR, 2023

Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information.

Alexander Lim, Fred Roosta

CoRR, 2023

The Interpolating Information Criterion for Overparameterized Models.

Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney

CoRR, 2023

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes.

Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney

Proceedings of the International Conference on Machine Learning, 2023

2022

MINRES: From Negative Curvature Detection to Monotonicity Properties.

Yang Liu, Fred Roosta

SIAM J. Optim., December, 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

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver.

Fred Roosta, Yang Liu, Peng Xu, Michael W. Mahoney

EURO J. Comput. Optim., 2022

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

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021

Inexact Nonconvex Newton-Type Methods.

Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney

INFORMS J. Optim., January, 2021

Convergence of Newton-MR under Inexact Hessian Information.

Yang Liu, Fred Roosta

SIAM J. Optim., 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

Implicit Langevin Algorithms for Sampling From Log-concave Densities.

Liam Hodgkinson, Robert Salomone, Fred Roosta

J. Mach. Learn. Res., 2021

Stochastic continuous normalizing flows: training SDEs as ODEs.

Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Non-PSD matrix sketching with applications to regression and optimization.

Zhili Feng, Fred Roosta, David P. Woodruff

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Shadow Manifold Hamiltonian Monte Carlo.

Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks.

Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Newton-type methods for non-convex optimization under inexact Hessian information.

Peng Xu, Fred Roosta, Michael W. Mahoney

Math. Program., 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

Stochastic Normalizing Flows.

Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney

CoRR, 2020

Second-order Optimization for Non-convex Machine Learning: an Empirical Study.

Peng Xu, Fred Roosta, Michael W. Mahoney

Proceedings of the 2020 SIAM International Conference on Data Mining, 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

Proceedings of the International Conference for High Performance Computing, 2020

DINO: Distributed Newton-Type Optimization Method.

Rixon Crane, Fred Roosta

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Sub-sampled Newton methods.

Farbod Roosta-Khorasani, Michael W. Mahoney

Math. Program., 2019

Variational perspective on local graph clustering.

Kimon Fountoulakis, Farbod Roosta-Khorasani, Julian Shun, Xiang Cheng, Michael W. Mahoney

Math. Program., 2019

Richer priors for infinitely wide multi-layer perceptrons.

Russell Tsuchida, Fred Roosta, Marcus Gallagher

CoRR, 2019

GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems.

Sudhir B. Kylasa, Fred (Farbod) Roosta, Michael W. Mahoney, Ananth Grama

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization.

Rixon Crane, Fred Roosta

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exchangeability and Kernel Invariance in Trained MLPs.

Russell Tsuchida, Fred (Farbod) Roosta, Marcus Gallagher

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Newton-MR: Newton's Method Without Smoothness or Convexity.

Fred (Farbod) Roosta, Yang Liu, Peng Xu, Michael W. Mahoney

CoRR, 2018

Distributed Second-order Convex Optimization.

Chih-Hao Fang, Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama

CoRR, 2018

GPU Accelerated Sub-Sampled Newton\textsf{'}s Method.

Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama

CoRR, 2018

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization.

Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Invariance of Weight Distributions in Rectified MLPs.

Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher

Proceedings of the 35th International Conference on Machine Learning, 2018

Out-of-sample extension of graph adjacency spectral embedding.

Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe

Proceedings of the 35th International Conference on Machine Learning, 2018

FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods.

Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Parallel Local Graph Clustering.

Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney

Proc. VLDB Endow., 2016

Sub-Sampled Newton Methods II: Local Convergence Rates.

Farbod Roosta-Khorasani, Michael W. Mahoney

CoRR, 2016

Sub-Sampled Newton Methods I: Globally Convergent Algorithms.

Farbod Roosta-Khorasani, Michael W. Mahoney

CoRR, 2016

FLAG: Fast Linearly-Coupled Adaptive Gradient Method.

Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney

CoRR, 2016

Sub-sampled Newton Methods with Non-uniform Sampling.

Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

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

Improved Bounds on Sample Size for Implicit Matrix Trace Estimators.

Farbod Roosta-Khorasani, Uri M. Ascher

Found. Comput. Math., 2015

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

Algorithms that satisfy a stopping criterion, probably.

Uri M. Ascher, Farbod Roosta-Khorasani

CoRR, 2014

2013

Data completion and stochastic algorithms for PDE inversion problems with many measurements.

Farbod Roosta-Khorasani, Kees van den Doel, Uri M. Ascher

CoRR, 2013