
Choosing the Sample with Lowest Loss makes SGD Robust
The presence of outliers can potentially significantly skew the paramete...
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Learning Distributions Generated by OneLayer ReLU Networks
We consider the problem of estimating the parameters of a ddimensional ...
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Extreme Multilabel Classification from Aggregated Labels
Extreme multilabel classification (XMC) is the problem of finding the r...
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The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
We characterize the effectiveness of a natural and classic algorithm for...
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Sparse Quadratic Logistic Regression in Subquadratic Time
We consider support recovery in the quadratic logistic regression settin...
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Single Pass PCA of Matrix Products
In this paper we present a new algorithm for computing a low rank approx...
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The Search Problem in Mixture Models
We consider the task of learning the parameters of a single component o...
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Nonsquare matrix sensing without spurious local minima via the BurerMonteiro approach
We consider the nonsquare matrix sensing problem, under restricted isom...
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Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization
We consider the problem of solving mixed random linear equations with k ...
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Provable BurerMonteiro factorization for a class of normconstrained matrix problems
We study the projected gradient descent method on lowrank matrix proble...
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Tradingoff variance and complexity in stochastic gradient descent
Stochastic gradient descent is the method of choice for largescale mach...
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Dropping Convexity for Faster Semidefinite Optimization
We study the minimization of a convex function f(X) over the set of n× n...
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The local convexity of solving systems of quadratic equations
This paper considers the recovery of a rank r positive semidefinite matr...
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Convergence Rates of Active Learning for Maximum Likelihood Estimation
An active learner is given a class of models, a large set of unlabeled e...
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A New Sampling Technique for Tensors
In this paper we propose new techniques to sample arbitrary thirdorder ...
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Greedy Subspace Clustering
We consider the problem of subspace clustering: given points that lie on...
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Nonconvex Robust PCA
We propose a new method for robust PCA  the task of recovering a lowr...
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Tighter Lowrank Approximation via Sampling the Leveraged Element
In this work, we propose a new randomized algorithm for computing a low...
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Completing Any Lowrank Matrix, Provably
Matrix completion, i.e., the exact and provable recovery of a lowrank m...
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Phase Retrieval using Alternating Minimization
Phase retrieval problems involve solving linear equations, but with miss...
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Lowrank Matrix Completion using Alternating Minimization
Alternating minimization represents a widely applicable and empirically ...
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Improved Graph Clustering
Graph clustering involves the task of dividing nodes into clusters, so t...
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Greedy Learning of Markov Network Structure
We propose a new yet natural algorithm for learning the graph structure ...
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Finding the Graph of Epidemic Cascades
We consider the problem of finding the graph on which an epidemic cascad...
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A Dirty Model for Multiple Sparse Regression
Sparse linear regression  finding an unknown vector from linear measur...
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Clustering Partially Observed Graphs via Convex Optimization
This paper considers the problem of clustering a partially observed unwe...
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Lowrank Matrix Recovery from Errors and Erasures
This paper considers the recovery of a lowrank matrix from an observed ...
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Matrix completion with column manipulation: Nearoptimal samplerobustnessrank tradeoffs
This paper considers the problem of matrix completion when some number o...
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Robust PCA via Outlier Pursuit
Singular Value Decomposition (and Principal Component Analysis) is one o...
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Provable quantum state tomography via nonconvex methods
With nowadays steadily growing quantum processors, it is required to dev...
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Iteratively Learning from the Best
We study a simple generic framework to address the issue of bad training...
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Minimum norm solutions do not always generalize well for overparameterized problems
Stochastic gradient descent is the de facto algorithm for training deep ...
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Iterative Least Trimmed Squares for Mixed Linear Regression
Given a linear regression setting, Iterative Least Trimmed Squares (ILTS...
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Blocking Bandits
We consider a novel stochastic multiarmed bandit setting, where playing...
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Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Subquadratic Time and Space
Quadratic regression involves modeling the response as a (generalized) l...
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Sujay Sanghavi
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Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, Assistant Professor at Electrical Engineering, University of Texas, Austin from 20092014, Visiting Scientist at Google Inc. 2014, Assistant Professor at Electrical Engineering, Purdue University from 20082009, Postdoctoral Scholar at Massachusetts Institute of Technology from 20062008, NSF CAREER award in January 2010.