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ORCIDSahand Negahban
Person information
- affiliation: Yale University, New Haven, CT, USA
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Books and Theses
- 2012
[b1]Sahand N. Negahban:
Structured Estimation In High-Dimensions. University of California, Berkeley, USA, 2012
Journal Articles
- 2018
- [j6]Uri Shaham, Yutaro Yamada, Sahand Negahban:
Understanding adversarial training: Increasing local stability of supervised models through robust optimization. Neurocomputing 307: 195-204 (2018) - [j5]Sahand Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Learning from Comparisons and Choices. J. Mach. Learn. Res. 19: 40:1-40:95 (2018) - 2017
- [j4]Sahand Negahban, Sewoong Oh
, Devavrat Shah:
Rank Centrality: Ranking from Pairwise Comparisons. Oper. Res. 65(1): 266-287 (2017) - [j3]Bobak Mortazavi, Nihar Desai, Jing Zhang, Andreas Coppi, Fred Warner, Harlan M. Krumholz, Sahand Negahban:
Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE J. Biomed. Health Informatics 21(6): 1719-1729 (2017) - 2012
- [j2]Sahand N. Negahban, Martin J. Wainwright:
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise. J. Mach. Learn. Res. 13: 1665-1697 (2012) - 2011
- [j1]Sahand N. Negahban, Martin J. Wainwright:
Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block 1/ INFINITY -Regularization. IEEE Trans. Inf. Theory 57(6): 3841-3863 (2011)
Conference and Workshop Papers
- 2021
- [c22]Qinghao Liang, Sahand Negahban, Joseph Chang, Harrison H. Zhou, Dustin Scheinost:
Connectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion. ISBI 2021: 738-742 - [c21]Dominic Richards, Sahand Negahban, Patrick Rebeschini:
Distributed Machine Learning with Sparse Heterogeneous Data. NeurIPS 2021: 18008-18020 - 2020
- [c20]Sheng Xu, Zhou Fan, Sahand Negahban:
Tree-projected gradient descent for estimating gradient-sparse parameters on graphs. COLT 2020: 3683-3708 - [c19]Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger:
Feature Selection using Stochastic Gates. ICML 2020: 10648-10659 - 2019
- [c18]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML 2019: 7335-7344 - 2018
- [c17]Satish M. Mahajan, Amey S. Mahajan, Sahand Negahban:
Regional Differences in Predicting Risk of 30-Day Readmissions for Heart Failure. Nursing Informatics 2018: 245-249 - [c16]Satish M. Mahajan, Amey S. Mahajan, Robert King, Sahand Negahban:
Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods. Nursing Informatics 2018: 250-255 - 2017
- [c15]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh:
Scalable Greedy Feature Selection via Weak Submodularity. AISTATS 2017: 1560-1568 - [c14]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban:
On Approximation Guarantees for Greedy Low Rank Optimization. ICML 2017: 1837-1846 - [c13]Addison Hu, Sahand Negahban:
Minimax Estimation of Bandable Precision Matrices. NIPS 2017: 4888-4896 - 2015
- [c12]Yu Lu, Sahand N. Negahban:
Individualized rank aggregation using nuclear norm regularization. Allerton 2015: 1473-1479 - 2014
- [c11]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. CISS 2014: 1-2 - 2012
- [c10]Sahand Negahban, Devavrat Shah:
Learning sparse Boolean polynomials. Allerton Conference 2012: 2032-2036 - [c9]Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell:
Scaling multiple-source entity resolution using statistically efficient transfer learning. CIKM 2012: 2224-2228 - [c8]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions. NIPS 2012: 1547-1555 - [c7]Sahand Negahban, Sewoong Oh, Devavrat Shah:
Iterative ranking from pair-wise comparisons. NIPS 2012: 2483-2491 - [c6]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
FASt global convergence of gradient methods for solving regularized M-estimation. SSP 2012: 409-412 - 2011
- [c5]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. ICML 2011: 1129-1136 - 2010
- [c4]Sahand N. Negahban, Martin J. Wainwright:
Estimation of (near) low-rank matrices with noise and high-dimensional scaling. ICML 2010: 823-830 - [c3]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Fast global convergence rates of gradient methods for high-dimensional statistical recovery. NIPS 2010: 37-45 - 2009
- [c2]Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu:
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. NIPS 2009: 1348-1356 - 2008
- [c1]Sahand N. Negahban, Martin J. Wainwright:
Phase transitions for high-dimensional joint support recovery. NIPS 2008: 1161-1168
Informal and Other Publications
- 2020
- [i17]Sheng Xu, Zhou Fan, Sahand Negahban:
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs. CoRR abs/2006.01662 (2020) - 2019
- [i16]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR abs/1901.00301 (2019) - 2018
- [i15]Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger:
Deep supervised feature selection using Stochastic Gates. CoRR abs/1810.04247 (2018) - [i14]Hamid Dadkhahi, Sahand Negahban:
Alternating Linear Bandits for Online Matrix-Factorization Recommendation. CoRR abs/1810.09401 (2018) - 2017
- [i13]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban:
On Approximation Guarantees for Greedy Low Rank Optimization. CoRR abs/1703.02721 (2017) - [i12]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh:
Scalable Greedy Feature Selection via Weak Submodularity. CoRR abs/1703.02723 (2017) - [i11]Sahand Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Learning from Comparisons and Choices. CoRR abs/1704.07228 (2017) - 2016
- [i10]Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand N. Negahban:
Restricted Strong Convexity Implies Weak Submodularity. CoRR abs/1612.00804 (2016) - 2015
- [i9]Uri Shaham, Yutaro Yamada, Sahand Negahban:
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. CoRR abs/1511.05432 (2015) - 2012
- [i8]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. CoRR abs/1207.4421 (2012) - [i7]Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell:
Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning. CoRR abs/1208.1860 (2012) - [i6]Sahand Negahban, Sewoong Oh, Devavrat Shah:
Iterative Ranking from Pair-wise Comparisons. CoRR abs/1209.1688 (2012) - 2011
- [i5]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. CoRR abs/1102.4807 (2011) - [i4]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Fast global convergence of gradient methods for high-dimensional statistical recovery. CoRR abs/1104.4824 (2011) - 2010
- [i3]Sahand N. Negahban, Martin J. Wainwright:
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise. CoRR abs/1009.2118 (2010) - [i2]Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu:
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. CoRR abs/1010.2731 (2010) - 2009
- [i1]Sahand N. Negahban, Martin J. Wainwright:
Simultaneous support recovery in high dimensions: Benefits and perils of block l1/linfinity-regularization. CoRR abs/0905.0642 (2009)
Coauthor Index
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