
CommunicationEfficient Asynchronous Stochastic FrankWolfe over Nuclearnorm Balls
Largescale machine learning training suffers from two prior challenges,...
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EM Algorithm is SampleOptimal for Learning Mixtures of WellSeparated Gaussians
We consider the problem of spherical Gaussian Mixture models with k ≥ 3 ...
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On the Minimax Optimality of the EM Algorithm for Learning TwoComponent Mixed Linear Regression
We study the convergence rates of the EM algorithm for learning twocomp...
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More Supervision, Less Computation: StatisticalComputational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where th...
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Contextual Blocking Bandits
We study a novel variant of the multiarmed bandit problem, where at eac...
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Robust Estimation of Tree Structured Ising Models
We consider the task of learning Ising models when the signs of differen...
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Mix and Match: An Optimistic TreeSearch Approach for Learning Models from Mixture Distributions
We consider a covariate shift problem where one has access to several m...
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High Dimensional Robust Sparse Regression
We provide a novel  and to the best of our knowledge, the first  alg...
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Robust compressed sensing of generative models
The goal of compressed sensing is to estimate a high dimensional vector ...
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Entropic Latent Variable Discovery
We consider the problem of discovering the simplest latent variable that...
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PrimalDual Block FrankWolfe
We propose a variant of the FrankWolfe algorithm for solving a class of...
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Disentangling Mixtures of Epidemics on Graphs
We consider the problem of learning the weighted edges of a mixture of t...
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Statistical inference using SGD
We present a novel method for frequentist statistical inference in Mest...
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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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Fast Algorithms for Robust PCA via Gradient Descent
We consider the problem of Robust PCA in the fully and partially observe...
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Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Latent variable models are a fundamental modeling tool in machine learni...
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Optimal linear estimation under unknown nonlinear transform
Linear regression studies the problem of estimating a model parameter β^...
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Greedy Subspace Clustering
We consider the problem of subspace clustering: given points that lie on...
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A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
We consider the mixed regression problem with two components, under adve...
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Memory Limited, Streaming PCA
We consider streaming, onepass principal component analysis (PCA), in t...
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Robust High Dimensional Sparse Regression and Matching Pursuit
We consider high dimensional sparse regression, and develop strategies a...
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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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The Stochastic Firefighter Problem
The dynamics of infectious diseases spread is crucial in determining the...
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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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The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations
We introduce a new malware detector  ShapeGD  that aggregates permac...
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Approximate Newtonbased statistical inference using only stochastic gradients
We present a novel inference framework for convex empirical risk minimiz...
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Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression
The ExpectationMaximization algorithm is perhaps the most broadly used ...
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Robust estimation of tree structured Gaussian Graphical Model
Consider jointly Gaussian random variables whose conditional independenc...
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Learning Graphs from Noisy Epidemic Cascades
We consider the problem of learning the weighted edges of a graph by obs...
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High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding
We study the problem of sparsity constrained Mestimation with arbitrary...
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EM Converges for a Mixture of Many Linear Regressions
We study the convergence of the ExpectationMaximization (EM) algorithm ...
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Constantine Caramanis
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Fluor Centennial Teaching Fellowship in Engineering #2, Associate Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin since 2006, Ph.D. in Electrical Engineering and Computer Science from MIT.