Sahand N. Negahban

24 papers A* 3A 1Misc 1Journal 12Unranked 6
YearRankTypeTitle / Venue / Authors
2019 J jnl
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.
CoRR
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban
2017 A* conf
On Approximation Guarantees for Greedy Low Rank Optimization.
ICML
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban
2017 J jnl
On Approximation Guarantees for Greedy Low Rank Optimization.
CoRR
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban
2017 A conf
Scalable Greedy Feature Selection via Weak Submodularity.
AISTATS
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
2017 J jnl
Scalable Greedy Feature Selection via Weak Submodularity.
CoRR
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
2016 J jnl
Restricted Strong Convexity Implies Weak Submodularity.
CoRR
Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand N. Negahban
2015 — conf
Individualized rank aggregation using nuclear norm regularization.
Allerton
Yu Lu, Sahand N. Negahban
2014 Misc conf
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions.
CISS
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012 — conf
FASt global convergence of gradient methods for solving regularized M-estimation.
SSP
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012 J jnl
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise.
J. Mach. Learn. Res.
Sahand N. Negahban, Martin J. Wainwright
2012 J jnl
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
CoRR
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012 — conf
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions.
NIPS
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012 — —
Structured Estimation In High-Dimensions.
Sahand N. Negahban
2011 J jnl
Fast global convergence of gradient methods for high-dimensional statistical recovery
CoRR
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011 J jnl
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
CoRR
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011 A* conf
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions.
ICML
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011 J jnl
Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block
IEEE Trans. Inf. Theory
Sahand N. Negahban, Martin J. Wainwright
2010 J jnl
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
CoRR
Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu
2010 A* conf
Estimation of (near) low-rank matrices with noise and high-dimensional scaling.
ICML
Sahand N. Negahban, Martin J. Wainwright
2010 — conf
Fast global convergence rates of gradient methods for high-dimensional statistical recovery.
NIPS
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2010 J jnl
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
CoRR
Sahand N. Negahban, Martin J. Wainwright
2009 — conf
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers.
NIPS
Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu
2009 J jnl
Simultaneous support recovery in high dimensions: Benefits and perils of block l
CoRR
Sahand N. Negahban, Martin J. Wainwright
2008 — conf
Phase transitions for high-dimensional joint support recovery.
NIPS
Sahand N. Negahban, Martin J. Wainwright