
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations
We present novel minibatch stochastic optimization methods for empirical...
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Exploiting Strong Convexity from Data with PrimalDual FirstOrder Algorithms
We consider empirical risk minimization of linear predictors with convex...
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Stochastic Canonical Correlation Analysis
We tightly analyze the sample complexity of CCA, provide a learning algo...
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Rate Optimal Estimation and Confidence Intervals for Highdimensional Regression with Missing Covariates
Although a majority of the theoretical literature in highdimensional st...
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Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and Highdimensional Data
Sketching techniques have become popular for scaling up machine learning...
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Warm Starting Bayesian Optimization
We develop a framework for warmstarting Bayesian optimization, that red...
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Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning i...
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A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern largescale machine learning applications, the training data a...
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Distributed MultiTask Learning with Shared Representation
We study the problem of distributed multitask learning with shared repr...
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MultiInformation Source Optimization
We consider Bayesian optimization of an expensivetoevaluate blackbox ...
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Parallel Bayesian Global Optimization of Expensive Functions
We consider parallel global optimization of derivativefree expensiveto...
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Reducing Runtime by Recycling Samples
Contrary to the situation with stochastic gradient descent, we argue tha...
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Distributed Multitask Learning
We consider the problem of distributed multitask learning, where each m...
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Bayesian optimization for materials design
We introduce Bayesian optimization, a technique developed for optimizing...
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Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite im...
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Inference for Sparse Conditional Precision Matrices
Given n i.i.d. observations of a random vector (X,Z), where X is a high...
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Efficient coordinatewise leading eigenvector computation
We develop and analyze efficient "coordinatewise" methods for finding t...
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Distributed Stochastic MultiTask Learning with Graph Regularization
We propose methods for distributed graphbased multitask learning that ...
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Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
We suggest a general oraclebased framework that captures different para...
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Gradient Sparsification for CommunicationEfficient Distributed Optimization
Modern large scale machine learning applications require stochastic opti...
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Jialei Wang
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5th year PhD student in Department of Computer Science, University of Chicago, bachelor's degree in SCGY , University of Science and Technology of China.