Skip to menu Skip to content Skip to footer
  • UQ Home
  • Contacts
  • Study
  • Maps
  • News
  • Events
  • Library
  • Give now
  • my.UQ
The University of Queensland
School of Mathematics and Physics School of Mathematics and Physics
Site search
Site search
Menu
  • Home
  • About
    • Our people
    • News
    • Events
    • Alumni
  • Mathematics
    • Mathematics people
    • Mathematics courses
  • Physics
    • Physics people
    • Physics courses
  • Study
    • Undergraduate
    • Honours
    • Postgraduate coursework
    • Higher degree by research
  • Research
    • Research centres
    • Industry engagement
    • Visiting fellowships
  • Outreach
    • Pitch Drop experiment
    • Mathematics summer school
    • Junior Physics Odyssey
    • School seminars and colloquia
  • Current students
    • Course lists
      • Course list 2021
      • Course list 2020
      • Course list 2019
      • Course list 2018
      • Course list 2017
      • Course list 2016
      • Course list 2015
      • Course list 2014
      • Course list 2013
      • Course list 2012
      • Course list 2011
      • Course list 2010
    • Scholarships and prizes
  • Contact
    • Staff directory

Dr Fred Roosta-Khorasani

ARC DECRA Fellowship
School of Mathematics and Physics
+61 7 336 53259
fred.roosta@uq.edu.au
Priestley Building (67), Room 447
View researcher profile

Personal page

Dr Fred Roosta-Khorasani's personal page

Publications

Book Chapters (2)
Journal Articles (9)
Conference Papers (11)
Department Technical Reports (3)

Book Chapters

Kylasa, Sudhir, Fang, Chih-Hao, Roosta, Fred and Grama, Ananth (2020). Parallel optimization techniques for machine learning. Parallel algorithms in computational science and engineering. (pp. 381-417) Cham, Switzerland: Birkhauser. doi: 10.1007/978-3-030-43736-7_13
Ye, Nan, Roosta-Khorasani, Farbod and Cui, Tiangang (2018). Optimization methods for inverse problems. 2017 MATRIX Annals. (pp. 121-140) edited by David R. Wood, Jan de Gier, Cheryl E. Praeger and Terence Tao. MATRIX Book Series: Springer.

Journal Articles

Xu, Peng, Roosta, Fred and Mahoney, Michael W. (2020). Newton-type methods for non-convex optimization under inexact Hessian information. Mathematical Programming, 184 (1-2), 35-70. doi: 10.1007/s10107-019-01405-z
Roosta-Khorasani, Farbod and Mahoney, Michael W. (2018). Sub-sampled Newton methods. Mathematical Programming, 174 (1-2), 293-326. doi: 10.1007/s10107-018-1346-5
Fountoulakis, Kimon, Roosta-Khorasani, Farbod, Shun, Julian, Cheng, Xiang and Mahoney, Michael W. (2017). Variational perspective on local graph clustering. Mathematical Programming, 174 (1-2), 553-573. doi: 10.1007/s10107-017-1214-8
Ascher, Uri and Roosta-Khorasani, Farbod (2016). Algorithms that satisfy a stopping criterion, probably. Vietnam Journal of Mathematics, 44 (1), 49-69. doi: 10.1007/s10013-015-0167-6
Roosta-Khorasani, Farbod and Szekely, Gábor J. (2015). Schur properties of convolutions of gamma random variables. Metrika, 78 (8), 997-1014. doi: 10.1007/s00184-015-0537-9
Roosta-Khorasani, Farbod and Ascher, Uri (2015). Improved bounds on sample size for implicit matrix trace estimators. Foundations of Computational Mathematics, 15 (5), 1187-1212. doi: 10.1007/s10208-014-9220-1
Roosta-Khorasani, Farbod, Székely, Gábor J. and Ascher, Uri M. (2015). Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables. SIAM/ASA Journal on Uncertainty Quantification, 3 (1), 61-90. doi: 10.1137/14096311X
Roosta-Khorasani, Farbod, van den Doel, Kees and Ascher, Uri (2014). Data completion and stochastic algorithms for PDE inversion problems with many measurements. Electronic Transactions on Numerical Analysis, 42, 177-196.
Roosta-Khorasani, Farbod, Van Den Doel, Kees and Ascher, Uri (2014). Stochastic algorithms for inverse problems involving pdes and many measurements. SIAM Journal on Scientific Computing, 36 (5), S3-S22. doi: 10.1137/130922756

Conference Papers

Xu, Peng, Roosta, Fred and Mahoney, Michael W. (2020). Second-order optimization for non-convex machine learning: an empirical study. SIAM International Conference on Data Mining, Cincinnati, OH, United States, 7-9 May 2020. Philadelphia, PA, United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611976236.23
Kylasa, Sudhir, Roosta, Fred (Farbod), Mahoney, Michael W. and Grama, Ananth (2019). GPU accelerated sub-sampled Newton's method for convex classification problems. SIAM International Conference on Data Mining, Calgary, Canada, 2-4 May 2019. Philadelphia, PA, United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611975673.79
Tsuchida, Russell, Roosta, Fred and Gallagher, Marcus (2019). Exchangeability and kernel invariance in trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, Macao, China, 10-16 August 2019. Marina del Rey, CA USA: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2019/498
Crane, Rixon and Roosta, Fred (2019). DINGO: Distributed Newton-type method for gradient-norm optimization. Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 8-14 December 2019. Maryland Heights, MO United States: Morgan Kaufmann Publishers.
Tsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press.
Cheng, Xiang, Roosta-Khorasani, Farbod, Palombo, Stefan, Bartlett, Peter L. and Mahoney, Michael W. (2018). FLAG n’ FLARE: fast linearly-coupled adaptive gradient methods. Twenty-First International Conference on Artificial Intelligence and Statistics, Lanzarote, Canary Islands, 9-11 April 2018. Cambridge, MA, United States: M I T Press.
Wang, Shusen, Roosta-Khorasani, Farbod, Xu, Peng and Mahoney, Michael W. (2018). GIANT: Globally improved approximate Newton method for distributed optimization. 32nd Conference on Neural Information Processing Systems, NeurIPS 2018, Montreal, QC, Canada, 2 - 8 December, 2018. Maryland Heights, MO, United States: Neural information processing systems foundation.
Levin, Keith, Roosta-Khorasani, Farbod, Mahoney, Michael W. and Priebe, Carey E. (2018). Out-of-sample extension of graph adjacency spectral embedding. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press.
Bouchard, Kristofer E, Bujan, Alejandro F, Roosta-Khorasani, Farbod, Prabhat, Snijders, Jian-Hua Mao, Chang, Edward F, Mahoney, Michael W and Bhattacharyya, Sharmodeep (2017). The Union of Intersections (UoI) method for interpretable data driven discovery and prediction. 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA United States, 4-9 December 2017. Maryland Heights, MO, United States: Morgan Kaufmann Publishers.
Xu, Peng, Yang, Jiyan, Roosta-Khorasani, Farbod, Re, Christopher and Mahoney, Michael (2016). Sub-sampled Newton methods with non-uniform sampling. Neural Information Processing Systems 2016, Barcelona Spain, 5 - 10 December 2016 . La Jolla, CA United States: Neural Information Processing Systems Foundation.
Shun, Julian, Roosta-Khorasani, Farbod, Fountoulakis, Kimon and Mahoney, Michael W. (2016). Parallel local graph clustering. International Conferenceon Very Large Data Bases, New Delhi, India, 5-9 September 2016. New York, United States: Association for Computing Machinery. doi: 10.14778/2994509.2994522

Department Technical Reports

Xu, Peng , Roosta-Khorasani, Farbod and Mahoney, Michael W. (2017). Newton-type methods for non-convex optimization under inexact Hessian information. Cornell University Library, Cornell University.
Xu, Peng, Roosta-Khorasani, Farbod and Mahoney, Michael W. (2017). Second order optimization for non-convex machine learning: an empirical study. Cornell University Library, Cornell University.
Cheng, Xiang , Roosta-Khorasani, Farbod , Bartlett, Peter L. and Mahoney, Michael W. (2016). FLAG: Fast Linearly-Coupled Adaptive Gradient method. Cornell University Library, Cornell University.

Areas of research

Applied mathematics
Operations research
Scientific computing and numerical modelling
Statistics and probability
© The University of Queensland
Enquiries: +61 7 3365 1111   |   Contact directory
ABN: 63 942 912 684   |   CRICOS Provider No: 00025B
Emergency
Phone: 3365 3333
Privacy & Terms of use   |   Feedback   |   Updated: 6 Mar 2021
Login