Mikael Johansson

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KTH Royal Institute of Technology

Advances in Asynchronous Parallel and Distributed Optimization

Motivated by large-scale optimization problems arising in the context of...
06/24/2020 ∙ by Mahmoud Assran, et al. ∙ 0 ∙ share

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Communication Efficient Sparsification for Large Scale Machine Learning

The increasing scale of distributed learning problems necessitates the d...
03/13/2020 ∙ by Sarit Khirirat, et al. ∙ 0 ∙ share

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Convergence of a Stochastic Gradient Method with Momentum for Nonsmooth Nonconvex Optimization

Stochastic gradient methods with momentum are widely used in application...
02/13/2020 ∙ by Vien V. Mai, et al. ∙ 0 ∙ share

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Anderson Acceleration of Proximal Gradient Methods

Anderson acceleration is a well-established and simple technique for spe...
10/18/2019 ∙ by Vien V. Mai, et al. ∙ 0 ∙ share

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Efficient Stochastic Programming in Julia

We present StochasticPrograms.jl, a user-friendly and powerful open-sour...
09/23/2019 ∙ by Martin Biel, et al. ∙ 0 ∙ share

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Noisy Accelerated Power Method for Eigenproblems with Applications

This paper introduces an efficient algorithm for finding the dominant ge...
03/20/2019 ∙ by Vien V. Mai, et al. ∙ 0 ∙ share

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Curvature-Exploiting Acceleration of Elastic Net Computations

This paper introduces an efficient second-order method for solving the e...
01/24/2019 ∙ by Vien V. Mai, et al. ∙ 0 ∙ share

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Harnessing the Power of Serverless Runtimes for Large-Scale Optimization

The event-driven and elastic nature of serverless runtimes makes them a ...
01/10/2019 ∙ by Arda Aytekin, et al. ∙ 0 ∙ share

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POLO: a POLicy-based Optimization library

We present POLO --- a C++ library for large-scale parallel optimization ...
10/08/2018 ∙ by Arda Aytekin, et al. ∙ 0 ∙ share

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The Convergence of Sparsified Gradient Methods

Distributed training of massive machine learning models, in particular d...
09/27/2018 ∙ by Dan Alistarh, et al. ∙ 0 ∙ share

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Distributed learning with compressed gradients

Asynchronous computation and gradient compression have emerged as two ke...
06/18/2018 ∙ by Sarit Khirirat, et al. ∙ 0 ∙ share

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Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

Motivated by the success of reinforcement learning (RL) for discrete-tim...
06/08/2018 ∙ by Motoya Ohnishi, et al. ∙ 0 ∙ share

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Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server

This paper presents an asynchronous incremental aggregated gradient algo...
10/18/2016 ∙ by Arda Aytekin, et al. ∙ 0 ∙ share

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An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization

Mini-batch optimization has proven to be a powerful paradigm for large-s...
05/18/2015 ∙ by Hamid Reza Feyzmahdavian, et al. ∙ 0 ∙ share

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Ergodic Mirror Descent

We generalize stochastic subgradient descent methods to situations in wh...
05/24/2011 ∙ by John C. Duchi, et al. ∙ 0 ∙ share

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Mikael Johansson

Featured Co-authors

Michael I. Jordan

Masashi Sugiyama

Torsten Hoefler

Alekh Agarwal

Dan Alistarh

John C. Duchi

Michael Rabbat

Cèdric Renggli

Mahmoud Assran

Arda Aytekin

Nikola Konstantinov

Advances in Asynchronous Parallel and Distributed Optimization

Communication Efficient Sparsification for Large Scale Machine Learning

Convergence of a Stochastic Gradient Method with Momentum for Nonsmooth Nonconvex Optimization

Anderson Acceleration of Proximal Gradient Methods

Efficient Stochastic Programming in Julia

Noisy Accelerated Power Method for Eigenproblems with Applications

Curvature-Exploiting Acceleration of Elastic Net Computations

Harnessing the Power of Serverless Runtimes for Large-Scale Optimization

POLO: a POLicy-based Optimization library

The Convergence of Sparsified Gradient Methods

Distributed learning with compressed gradients

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server

An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization

Ergodic Mirror Descent