![](https://dblp1.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp1.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp1.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp1.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp1.uni-trier.de/img/search.dark.16x16.png)
default search action
Sahand Negahban
Person information
- affiliation: Yale University, New Haven, CT, USA
Refine list
![note](https://dblp1.uni-trier.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 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 - [i17]Sheng Xu, Zhou Fan, Sahand Negahban:
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs. CoRR abs/2006.01662 (2020)
2010 – 2019
- 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 - [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
- [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) - [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 - [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
- [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) - [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 - [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
- [c12]Yu Lu, Sahand N. Negahban:
Individualized rank aggregation using nuclear norm regularization. Allerton 2015: 1473-1479 - [i9]Uri Shaham, Yutaro Yamada, Sahand Negahban:
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. CoRR abs/1511.05432 (2015) - 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
- [b1]Sahand N. Negahban:
Structured Estimation In High-Dimensions. University of California, Berkeley, USA, 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) - [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 - [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
- [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) - [c5]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. ICML 2011: 1129-1136 - [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
- [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 - [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)
2000 – 2009
- 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 - [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) - 2008
- [c1]Sahand N. Negahban, Martin J. Wainwright:
Phase transitions for high-dimensional joint support recovery. NIPS 2008: 1161-1168
Coauthor Index
![](https://dblp1.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-24 23:18 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint