Negahban, Sahand

14 publications

NeurIPS 2021 Distributed Machine Learning with Sparse Heterogeneous Data Dominic Richards, Sahand Negahban, Patrick Rebeschini

NeurIPSW 2021 Exploiting 3D Shape Bias Towards Robust Vision Yutaro Yamada, Yuval Kluger, Sahand Negahban, Ilker Yildirim

ICML 2020 Feature Selection Using Stochastic Gates Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

COLT 2020 Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs Sheng Xu, Zhou Fan, Sahand Negahban

ICML 2019 Warm-Starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback Chicheng Zhang, Alekh Agarwal, Hal Daumé Iii, John Langford, Sahand Negahban

JMLR 2018 Learning from Comparisons and Choices Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

NeurIPS 2017 Minimax Estimation of Bandable Precision Matrices Addison Hu, Sahand Negahban

ICML 2017 On Approximation Guarantees for Greedy Low Rank Optimization Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand Negahban

NeurIPS 2012 Iterative Ranking from Pair-Wise Comparisons Sahand Negahban, Sewoong Oh, Devavrat Shah

JMLR 2012 Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise Sahand Negahban, Martin J. Wainwright

NeurIPS 2012 Stochastic Optimization and Sparse Statistical Recovery: Optimal Algorithms for High Dimensions Alekh Agarwal, Sahand Negahban, Martin J. Wainwright

NeurIPS 2010 Fast Global Convergence Rates of Gradient Methods for High-Dimensional Statistical Recovery Alekh Agarwal, Sahand Negahban, Martin J. Wainwright

NeurIPS 2009 A Unified Framework for High-Dimensional Analysis of $m$-Estimators with Decomposable Regularizers Sahand Negahban, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar

NeurIPS 2008 Phase Transitions for High-Dimensional Joint Support Recovery Sahand Negahban, Martin J. Wainwright