Dr Fred Roosta-Khorasani
Senior Lecturer
Mathematics
+61 7 336 53259
Priestley Building (67), Room 447
Personal page
Publications
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
Hodgkinson, Liam, Salomone, Robert and Roosta, Fred (2021). Implicit Langevin algorithms for sampling from log-concave densities. Journal of Machine Learning Research, 22 136, 1-30.
Potgieter, A. B., Zhao, Yan, Zarco-Tejada, Pablo J, Chenu, Karine, Zhang, Yifan, Porker, Kenton, Biddulph, Ben, Dang, Yash P., Neale, Tim, Roosta, Fred and Chapman, Scott (2021). Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. In Silico Plants, 3 (1) diab017, 1-23. doi: 10.1093/insilicoplants/diab017
Yao, Zhewei, Xu, Peng, Roosta, Fred and Mahoney, Michael W. (2021). Inexact nonconvex Newton-type methods. INFORMS Journal on Optimization, 3 (2), 154-182. doi: 10.1287/ijoo.2019.0043
Liu, Yang and Roosta, Fred (2021). Convergence of Newton-mr under inexact hessian information. SIAM Journal on Optimization, 31 (1), 59-90. doi: 10.1137/19M1302211
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
van der Heide, Chris, Hodgkinson, Liam, Roosta, Fred and Kroese, Dirk (2021). Shadow Manifold Hamiltonian Monte Carlo. International Conference on Artificial Intelligence and Statistics, Online, 27-30- July 2021. Tempe, AZ, United States: ML Research Press.
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
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, 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
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
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.
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.
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.
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.