Phase transitions for high-dimensional joint support recovery. - Sahand N. Negahban; Martin J. Wainwright (NIPS, 2008)

A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. - Sahand N. Negahban; Pradeep Ravikumar; Martin J. Wainwright; Bin Yu 001 (NIPS, 2009)

Simultaneous support recovery in high dimensions: Benefits and perils of block l1/linfinity-regularization - Sahand N. Negahban; Martin J. Wainwright (CoRR, 2009)

Estimation of (near) low-rank matrices with noise and high-dimensional scaling. - Sahand N. Negahban; Martin J. Wainwright (ICML, 2010)

Fast global convergence rates of gradient methods for high-dimensional statistical recovery. - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (NIPS, 2010)

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (ICML, 2011)

Fast global convergence of gradient methods for high-dimensional statistical recovery - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (CoRR, 2011)

Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block 1/ INFINITY -Regularization. - Sahand N. Negahban; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2011)

Learning sparse Boolean polynomials. - Sahand Negahban; Devavrat Shah (Allerton Conference, 2012)

Scaling multiple-source entity resolution using statistically efficient transfer learning. - Sahand Negahban; Benjamin I. P. Rubinstein; Jim Gemmell (CIKM, 2012)

Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions. - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (NIPS, 2012)

Iterative ranking from pair-wise comparisons. - Sahand Negahban; Sewoong Oh; Devavrat Shah (NIPS, 2012)

FASt global convergence of gradient methods for solving regularized M-estimation. - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (SSP, 2012)

Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise. - Sahand N. Negahban; Martin J. Wainwright (J. Mach. Learn. Res., 2012)

Structured Estimation In High-Dimensions. - Sahand N. Negahban (2012)

Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. - Alekh Agarwal; Sahand N. Negahban; Martin J. Wainwright (CISS, 2014)

Individualized rank aggregation using nuclear norm regularization. - Yu Lu; Sahand N. Negahban (Allerton, 2015)

Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. - Uri Shaham 001; Yutaro Yamada; Sahand Negahban (CoRR, 2015)

Restricted Strong Convexity Implies Weak Submodularity. - Ethan R. Elenberg; Rajiv Khanna; Alexandros G. Dimakis; Sahand N. Negahban (CoRR, 2016)

Scalable Greedy Feature Selection via Weak Submodularity. - Rajiv Khanna; Ethan R. Elenberg; Alexandros G. Dimakis; Sahand N. Negahban; Joydeep Ghosh (AISTATS, 2017)

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

Minimax Estimation of Bandable Precision Matrices. - Addison Hu; Sahand Negahban (NIPS, 2017)

On Approximation Guarantees for Greedy Low Rank Optimization. - Rajiv Khanna; Ethan R. Elenberg; Alexandros G. Dimakis; Sahand N. Negahban (CoRR, 2017)

Rank Centrality: Ranking from Pairwise Comparisons. - Sahand Negahban; Sewoong Oh; Devavrat Shah (Oper. Res., 2017)

Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. - Bobak Mortazavi; Nihar Desai; Jing Zhang; Andreas Coppi; Fred Warner; Harlan M. Krumholz; Sahand Negahban (IEEE J. Biomed. Health Informatics, 2017)

Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods. - Satish M. Mahajan; Amey S. Mahajan; Robert King; Sahand Negahban (Nursing Informatics, 2018)

Regional Differences in Predicting Risk of 30-Day Readmissions for Heart Failure. - Satish M. Mahajan; Amey S. Mahajan; Sahand Negahban (Nursing Informatics, 2018)

Deep supervised feature selection using Stochastic Gates. - Yutaro Yamada; Ofir Lindenbaum; Sahand Negahban; Yuval Kluger (CoRR, 2018)

Alternating Linear Bandits for Online Matrix-Factorization Recommendation. - Hamid Dadkhahi; Sahand Negahban (CoRR, 2018)

Understanding adversarial training: Increasing local stability of supervised models through robust optimization. - Uri Shaham 001; Yutaro Yamada; Sahand Negahban (Neurocomputing, 2018)

Learning from Comparisons and Choices. - Sahand Negahban; Sewoong Oh; Kiran Koshy Thekumparampil; Jiaming Xu 002 (J. Mach. Learn. Res., 2018)

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. - Chicheng Zhang; Alekh Agarwal; Hal Daumé III; John Langford 001; Sahand Negahban (ICML, 2019)

Tree-projected gradient descent for estimating gradient-sparse parameters on graphs. - Sheng Xu; Zhou Fan; Sahand Negahban (COLT, 2020)

Feature Selection using Stochastic Gates. - Yutaro Yamada; Ofir Lindenbaum; Sahand Negahban; Yuval Kluger (ICML, 2020)

Connectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion. - Qinghao Liang; Sahand Negahban; Joseph Chang; Harrison H. Zhou; Dustin Scheinost (ISBI, 2021)

Distributed Machine Learning with Sparse Heterogeneous Data. - Dominic Richards; Sahand Negahban; Patrick Rebeschini (NeurIPS, 2021)