Sahand Negahban
14 papers · 2008–2021 · 5 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(14)
Mathematics & Optimization
(12)
Optimization & Theory
(12)
Optimization
(10)
Core Methods
(8)
Artificial Intelligence
(4)
Learning Types
(4)
Mathematics
(4)
Data Science & Analytics
(2)
Applications
(2)
Core AI
(2)
Learning Paradigms
(2)
Computer Science
(1)
Deep Learning
(1)
Foundations
(1)
Keywords
restricted strong convexity
(4)
convergence rate
(3)
convex optimization
(2)
greedy algorithm
(2)
matrix completion
(2)
sparse regression
(2)
high-dimensional statistics
(2)
sparse optimization
(2)
matrix estimation
(2)
feature selection
(2)
gradient descent
(2)
preference learning
(2)
global convergence
(1)
supervised learning
(1)
sparse recovery
(1)
collaborative filtering
(1)
learning theory
(1)
statistical learning
(1)
distributed optimization
(1)
sparse learning
(1)
Papers
Distributed Machine Learning with Sparse Heterogeneous Data
NeurIPS 2021
Feature Selection using Stochastic Gates
ICML 2020
Tree-projected gradient descent for estimating gradient-sparse parameters on graphs
COLT 2020
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
ICML 2019
Learning from Comparisons and Choices
JMLR 2018
Minimax Estimation of Bandable Precision Matrices
NeurIPS 2017
On Approximation Guarantees for Greedy Low Rank Optimization
ICML 2017
Scalable Greedy Feature Selection via Weak Submodularity
AISTATS 2017
Iterative ranking from pair-wise comparisons
NeurIPS 2012
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
JMLR 2012
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
NeurIPS 2012
Fast global convergence rates of gradient methods for high-dimensional statistical recovery
NeurIPS 2010
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
NeurIPS 2009
Phase transitions for high-dimensional joint support recovery
NeurIPS 2008