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ORCIDFred (Farbod) Roosta
Person information
- affiliation: University of Queensland, School of Mathematics and Physics, Brisbane, Australia
- affiliation: UC Berkeley, International Computer Science Institute, CA, USA
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2020 – today
- 2024
[j15]Albert S. Berahas, Lindon Roberts, Fred Roosta:
Non-Uniform Smoothness for Gradient Descent. Trans. Mach. Learn. Res. 2024 (2024)- [c20]Oscar Smee, Fred Roosta:
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints. ICML 2024 - [c19]Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta:
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. ICML 2024 - [i34]Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney:
SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. CoRR abs/2401.00122 (2024) - [i33]Alexander Lim, Yang Liu, Fred Roosta:
Conjugate Direction Methods Under Inconsistent Systems. CoRR abs/2401.11714 (2024) - [i32]Hossein Askari, Fred Roosta, Hongfu Sun:
Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems. CoRR abs/2404.03706 (2024) - [i31]Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta:
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. CoRR abs/2405.09800 (2024) - 2023
- [c18]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML 2023: 13085-13117 - [i30]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
The Interpolating Information Criterion for Overparameterized Models. CoRR abs/2307.07785 (2023) - [i29]Alexander Lim, Fred Roosta:
Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information. CoRR abs/2308.09912 (2023) - [i28]Yang Liu, Andre Milzarek, Fred Roosta:
Obtaining Pseudo-inverse Solutions With MINRES. CoRR abs/2309.17096 (2023) - [i27]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR abs/2311.07013 (2023) - [i26]Albert S. Berahas, Lindon Roberts, Fred Roosta:
Non-Uniform Smoothness for Gradient Descent. CoRR abs/2311.08615 (2023) - 2022
- [j14]Fred Roosta
, Yang Liu, Peng Xu, Michael W. Mahoney:
Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. EURO J. Comput. Optim. 10: 100035 (2022) - [j13]Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney:
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. J. Mach. Learn. Res. 23: 22:1-22:36 (2022) - [j12]Yang Liu, Fred Roosta:
MINRES: From Negative Curvature Detection to Monotonicity Properties. SIAM J. Optim. 32(4): 2636-2661 (2022) - [c17]Dung Nguyen, Yan Zhao, Yifan Zhang, Anh Ngoc-Lan Huynh, Fred Roosta, Graeme L. Hammer, Scott C. Chapman, Andries B. Potgieter:
Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia. IGARSS 2022: 2742-2745 - [i25]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR abs/2210.07612 (2022) - 2021
- [j11]Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney:
Inexact Nonconvex Newton-Type Methods. INFORMS J. Optim. 3(2): 154-182 (2021) - [j10]Liam Hodgkinson, Robert Salomone, Fred Roosta:
Implicit Langevin Algorithms for Sampling From Log-concave Densities. J. Mach. Learn. Res. 22: 136:1-136:30 (2021) - [j9]Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe:
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. J. Mach. Learn. Res. 22: 194:1-194:59 (2021) - [j8]Yang Liu, Fred Roosta:
Convergence of Newton-MR under Inexact Hessian Information. SIAM J. Optim. 31(1): 59-90 (2021) - [c16]Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher:
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. AAAI 2021: 9967-9977 - [c15]Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese:
Shadow Manifold Hamiltonian Monte Carlo. AISTATS 2021: 1477-1485 - [c14]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic continuous normalizing flows: training SDEs as ODEs. UAI 2021: 1130-1140 - [c13]Zhili Feng, Fred Roosta, David P. Woodruff:
Non-PSD matrix sketching with applications to regression and optimization. UAI 2021: 1841-1851 - [i24]Zhili Feng, Fred Roosta, David P. Woodruff:
Non-PSD Matrix Sketching with Applications to Regression and Optimization. CoRR abs/2106.08544 (2021) - 2020
- [j7]Peng Xu, Fred Roosta, Michael W. Mahoney:
Newton-type methods for non-convex optimization under inexact Hessian information. Math. Program. 184(1): 35-70 (2020) - [c12]Rixon Crane, Fred Roosta:
DINO: Distributed Newton-Type Optimization Method. ICML 2020: 2174-2184 - [c11]Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama:
Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. SC 2020: 57 - [c10]Peng Xu, Fred Roosta, Michael W. Mahoney:
Second-order Optimization for Non-convex Machine Learning: an Empirical Study. SDM 2020: 199-207 - [i23]Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher:
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. CoRR abs/2002.08517 (2020) - [i22]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic Normalizing Flows. CoRR abs/2002.09547 (2020) - [i21]Vektor Dewanto, George Dunn, Ali Eshragh, Marcus Gallagher, Fred Roosta:
Average-reward model-free reinforcement learning: a systematic review and literature mapping. CoRR abs/2010.08920 (2020)
2010 – 2019
- 2019
- [j6]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-sampled Newton methods. Math. Program. 174(1-2): 293-326 (2019) - [j5]Kimon Fountoulakis, Farbod Roosta-Khorasani, Julian Shun, Xiang Cheng, Michael W. Mahoney:
Variational perspective on local graph clustering. Math. Program. 174(1-2): 553-573 (2019) - [c9]Russell Tsuchida, Fred (Farbod) Roosta, Marcus Gallagher:
Exchangeability and Kernel Invariance in Trained MLPs. IJCAI 2019: 3592-3598 - [c8]Rixon Crane, Fred Roosta:
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. NeurIPS 2019: 9494-9504 - [c7]Sudhir B. Kylasa, Fred (Farbod) Roosta, Michael W. Mahoney, Ananth Grama:
GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. SDM 2019: 702-710 - [i20]Rixon Crane, Fred (Farbod) Roosta:
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. CoRR abs/1901.05134 (2019) - [i19]Liam Hodgkinson, Robert Salomone, Fred (Farbod) Roosta:
Implicit Langevin Algorithms for Sampling From Log-concave Densities. CoRR abs/1903.12322 (2019) - [i18]Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe:
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. CoRR abs/1910.00423 (2019) - [i17]Russell Tsuchida, Fred Roosta, Marcus Gallagher:
Richer priors for infinitely wide multi-layer perceptrons. CoRR abs/1911.12927 (2019) - 2018
- [c6]Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney:
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. AISTATS 2018: 404-414 - [c5]Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe:
Out-of-sample extension of graph adjacency spectral embedding. ICML 2018: 2981-2990 - [c4]Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher:
Invariance of Weight Distributions in Rectified MLPs. ICML 2018: 5002-5011 - [c3]Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney:
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. NeurIPS 2018: 2338-2348 - [i16]Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama:
GPU Accelerated Sub-Sampled Newton\textsf{'}s Method. CoRR abs/1802.09113 (2018) - [i15]Chih-Hao Fang, Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama:
Distributed Second-order Convex Optimization. CoRR abs/1807.07132 (2018) - [i14]Fred (Farbod) Roosta, Yang Liu, Peng Xu, Michael W. Mahoney:
Newton-MR: Newton's Method Without Smoothness or Convexity. CoRR abs/1810.00303 (2018) - [i13]Russell Tsuchida, Fred (Farbod) Roosta, Marcus Gallagher:
Exchangeability and Kernel Invariance in Trained MLPs. CoRR abs/1810.08351 (2018) - 2017
- [c2]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:
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS 2017: 1078-1086 - [i12]Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney:
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information. CoRR abs/1708.07164 (2017) - [i11]Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney:
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study. CoRR abs/1708.07827 (2017) - [i10]Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney:
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. CoRR abs/1709.03528 (2017) - [i9]Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher:
Invariance of Weight Distributions in Rectified MLPs. CoRR abs/1711.09090 (2017) - 2016
- [j4]Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney:
Parallel Local Graph Clustering. Proc. VLDB Endow. 9(12): 1041-1052 (2016) - [c1]Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney:
Sub-sampled Newton Methods with Non-uniform Sampling. NIPS 2016: 3000-3008 - [i8]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-Sampled Newton Methods I: Globally Convergent Algorithms. CoRR abs/1601.04737 (2016) - [i7]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-Sampled Newton Methods II: Local Convergence Rates. CoRR abs/1601.04738 (2016) - [i6]Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney:
Parallel Local Graph Clustering. CoRR abs/1604.07515 (2016) - [i5]Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney:
FLAG: Fast Linearly-Coupled Adaptive Gradient Method. CoRR abs/1605.08108 (2016) - 2015
- [j3]Farbod Roosta-Khorasani, Uri M. Ascher:
Improved Bounds on Sample Size for Implicit Matrix Trace Estimators. Found. Comput. Math. 15(5): 1187-1212 (2015) - [j2]Farbod Roosta-Khorasani, Gábor J. Székely, Uri M. Ascher:
Assessing Stochastic Algorithms for Large Scale Nonlinear Least Squares Problems Using Extremal Probabilities of Linear Combinations of Gamma Random Variables. SIAM/ASA J. Uncertain. Quantification 3(1): 61-90 (2015) - 2014
- [j1]Farbod Roosta-Khorasani, Kees van den Doel, Uri M. Ascher:
Stochastic Algorithms for Inverse Problems Involving PDEs and many Measurements. SIAM J. Sci. Comput. 36(5) (2014) - [i4]Farbod Roosta-Khorasani, Gábor J. Székely, Uri M. Ascher:
Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables. CoRR abs/1404.0122 (2014) - [i3]Uri M. Ascher, Farbod Roosta-Khorasani:
Algorithms that satisfy a stopping criterion, probably. CoRR abs/1408.5946 (2014) - 2013
- [i2]Farbod Roosta-Khorasani, Uri M. Ascher:
Improved bounds on sample size for implicit matrix trace estimators. CoRR abs/1308.2475 (2013) - [i1]Farbod Roosta-Khorasani, Kees van den Doel, Uri M. Ascher:
Data completion and stochastic algorithms for PDE inversion problems with many measurements. CoRR abs/1312.0707 (2013)
Coauthor Index
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