Dr Fred Roosta-Khorasani
ARC DECRA Fellowship
School of Mathematics and Physics
+61 7 336 53259
Priestley Building #67, Room 447
Personal page
Publications
Book Chapter
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. (2019). Newton-type methods for non-convex optimization under inexact Hessian information. Mathematical Programming 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, 42177-196.
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
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.
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.
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.
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.