Farbod Roosta-Khorasani

25 papers A* 3Journal 20Unranked 2

Year	Rank	Type	Title / Venue / Authors
2019	J	`jnl`	Sub-sampled Newton methods. Math. Program. Farbod Roosta-Khorasani, Michael W. Mahoney
2019	J	`jnl`	Variational perspective on local graph clustering. Math. Program. Kimon Fountoulakis, Farbod Roosta-Khorasani, Julian Shun, Xiang Cheng, Michael W. Mahoney
2018	J	`jnl`	Distributed Second-order Convex Optimization. CoRR Chih-Hao Fang, Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama
2018	A*	`conf`	GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. NeurIPS Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney
2018	J	`jnl`	GPU Accelerated Sub-Sampled Newton\textsf{'}s Method. CoRR Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama
2018	A*	`conf`	Invariance of Weight Distributions in Rectified MLPs. ICML Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher
2018	A*	`conf`	Out-of-sample extension of graph adjacency spectral embedding. ICML Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe
2017	J	`jnl`	GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. CoRR Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney
2017	J	`jnl`	Invariance of Weight Distributions in Rectified MLPs. CoRR Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher
2017	J	`jnl`	Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information. CoRR Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
2017	J	`jnl`	Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study. CoRR Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
2017	—	`conf`	Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS 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
2016	J	`jnl`	FLAG: Fast Linearly-Coupled Adaptive Gradient Method. CoRR Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney
2016	J	`jnl`	Parallel Local Graph Clustering. CoRR Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney
2016	J	`jnl`	Parallel Local Graph Clustering. Proc. VLDB Endow. Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney
2016	J	`jnl`	Sub-Sampled Newton Methods I: Globally Convergent Algorithms. CoRR Farbod Roosta-Khorasani, Michael W. Mahoney
2016	J	`jnl`	Sub-Sampled Newton Methods II: Local Convergence Rates. CoRR Farbod Roosta-Khorasani, Michael W. Mahoney
2016	—	`conf`	Sub-sampled Newton Methods with Non-uniform Sampling. NIPS Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney
2015	J	`jnl`	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 Farbod Roosta-Khorasani, Gábor J. Székely, Uri M. Ascher
2015	J	`jnl`	Improved Bounds on Sample Size for Implicit Matrix Trace Estimators. Found. Comput. Math. Farbod Roosta-Khorasani, Uri M. Ascher
2014	J	`jnl`	Algorithms that satisfy a stopping criterion, probably. CoRR Uri M. Ascher, Farbod Roosta-Khorasani
2014	J	`jnl`	Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables. CoRR Farbod Roosta-Khorasani, Gábor J. Székely, Uri M. Ascher
2014	J	`jnl`	Stochastic Algorithms for Inverse Problems Involving PDEs and many Measurements. SIAM J. Sci. Comput. Farbod Roosta-Khorasani, Kees van den Doel, Uri M. Ascher
2013	J	`jnl`	Data completion and stochastic algorithms for PDE inversion problems with many measurements. CoRR Farbod Roosta-Khorasani, Kees van den Doel, Uri M. Ascher
2013	J	`jnl`	Improved bounds on sample size for implicit matrix trace estimators. CoRR Farbod Roosta-Khorasani, Uri M. Ascher