Dr Fred Roosta-Khorasani
ARC DECRA Fellowship
School of Mathematics and Physics
+61 7 336 53259
Priestley Building (67), Room 548
Personal page
Publications
Book Chapter
Ye, Nan, Roosta-Khorasani, Farbod and Cui, Tiangang (2018). Optimization methods for inverse problems. In David R. Wood, Jan de Gier, Cheryl E. Praeger and Terence Tao (Ed.), 2017 MATRIX Annals (pp. 1-19) MATRIX Book Series: Springer.
Journal Articles
Fountoulakis, Kimon, Roosta-Khorasani, Farbod, Shun, Julian, Cheng, Xiang and Mahoney, Michael W. (2017) Variational perspective on local graph clustering. Mathematical Programming, . 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) Stochastic algorithms for inverse problems involving pdes and many measurements. SIAM Journal on Scientific Computing, 36 5: S3-S22. doi:10.1137/130922756
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.
Conference Papers
Levin, Keith, Roosta, Fred, Mahoney, Michael W. and Priebe, Carey E. (2018). Out-of-sample extension of graph adjacency spectral embedding. In: Jennifer Dy and Andreas Krause, Proceedings of the 35th International Conference on Machine Learning. 35th International Conference on Machine Learning, Stockholm, Sweden, (). 10-15 July 2018.
Tsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. In: Jennifer Dy and Andreas Krause, Proceedings of the 35th International Conference on Machine Learning. 35th International Conference on Machine Learning, Stockholm, Sweden, (). 10-15 July 2018.
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. In: 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA United States, (). 4-9 December 2017.
Shun, Julian, Roosta-Khorasani, Farbod, Fountoulakis, Kimon and Mahoney, Michael W. (2016). Parallel local graph clustering. In: Surajit Chaudhuri and Jayant Haritsa, Proceedings of the 42nd International Conference!on Very Large Data Bases. International Conferenceon Very Large Data Bases, New Delhi, India, (1041-1052). 5-9 September 2016. doi:10.14778/2994509.2994522
Xu, Peng, Yang, Jiyan, Roosta-Khorasani, Farbod, Re, Christopher and Mahoney, Michael (2016). Sub-sampled Newton methods with non-uniform sampling. In: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon and R. Garnett, Advances in Neural Information Processing Systems 30. Neural Information Processing Systems 2016, Barcelona Spain, (2530-2538). 5 - 10 December 2016 .
Department Technical Reports
Wang, Shusen , Roosta-Khorasani, Farbod , Xu, Peng and Mahoney, Michael W. (2017) GIANT: Globally Improved Approximate Newton method for distributed optimization. , Cornell University Library, Cornell University.
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.