Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[i32]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)- [i31]Alexander Lim, Yang Liu, Fred Roosta:
Conjugate Direction Methods Under Inconsistent Systems. CoRR abs/2401.11714 (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 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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-21 17:21 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
