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05/25/2018
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
We suggest a general oracle-based framework that captures different para...
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02/11/2018
Distributed Stochastic Multi-Task Learning with Graph Regularization
We propose methods for distributed graph-based multi-task learning that ...
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10/26/2017
Gradient Sparsification for Communication-Efficient Distributed Optimization
Modern large scale machine learning applications require stochastic opti...
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06/21/2017
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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03/07/2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
We consider empirical risk minimization of linear predictors with convex...
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02/25/2017
Efficient coordinate-wise leading eigenvector computation
We develop and analyze efficient "coordinate-wise" methods for finding t...
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02/21/2017
Stochastic Canonical Correlation Analysis
We tightly analyze the sample complexity of CCA, provide a learning algo...
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02/09/2017
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
Although a majority of the theoretical literature in high-dimensional st...
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10/10/2016
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
Sketching techniques have become popular for scaling up machine learning...
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08/11/2016
Warm Starting Bayesian Optimization
We develop a framework for warm-starting Bayesian optimization, that red...
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05/25/2016
Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning i...
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04/13/2016
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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04/13/2016
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern large-scale machine learning applications, the training data a...
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03/07/2016
Distributed Multi-Task Learning with Shared Representation
We study the problem of distributed multi-task learning with shared repr...
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03/01/2016
Multi-Information Source Optimization
We consider Bayesian optimization of an expensive-to-evaluate black-box ...
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02/16/2016
Parallel Bayesian Global Optimization of Expensive Functions
We consider parallel global optimization of derivative-free expensive-to...
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02/05/2016
Reducing Runtime by Recycling Samples
Contrary to the situation with stochastic gradient descent, we argue tha...
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10/02/2015
Distributed Multitask Learning
We consider the problem of distributed multi-task learning, where each m...
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06/03/2015
Bayesian optimization for materials design
We introduce Bayesian optimization, a technique developed for optimizing...
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12/24/2014