Nicolas Loizou

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PhD student at the University of Edinburgh

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AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods

We present an adaptive stochastic variance reduced method with an implic...
02/19/2021 ∙ by Zheng Shi, et al. ∙ 12 ∙ share

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Stochastic Hamiltonian Gradient Methods for Smooth Games

The success of adversarial formulations in machine learning has brought ...
07/08/2020 ∙ by Nicolas Loizou, et al. ∙ 45 ∙ share

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Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

We present a unified theorem for the convergence analysis of stochastic ...
06/20/2020 ∙ by Ahmed Khaled, et al. ∙ 30 ∙ share

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SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

We provide several convergence theorems for SGD for two large classes of...
06/18/2020 ∙ by Robert M. Gower, et al. ∙ 17 ∙ share

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A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Decentralized stochastic optimization methods have gained a lot of atten...
03/23/2020 ∙ by Anastasia Koloskova, et al. ∙ 0 ∙ share

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Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence

We propose a stochastic variant of the classical Polyak step-size (Polya...
02/24/2020 ∙ by Nicolas Loizou, et al. ∙ 13 ∙ share

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Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip

In the era of big data, one of the key challenges is the development of ...
09/26/2019 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols

In this work we present a new framework for the analysis and design of r...
05/20/2019 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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Convergence Analysis of Inexact Randomized Iterative Methods

In this paper we present a convergence rate analysis of inexact variants...
03/19/2019 ∙ by Nicolas Loizou, et al. ∙ 11 ∙ share

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SGD: General Analysis and Improved Rates

We propose a general yet simple theorem describing the convergence of SG...
01/27/2019 ∙ by Robert Mansel Gower, et al. ∙ 5 ∙ share

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A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion

In this work we present a randomized gossip algorithm for solving the av...
01/27/2019 ∙ by Filip Hanzely, et al. ∙ 0 ∙ share

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Stochastic Gradient Push for Distributed Deep Learning

Large mini-batch parallel SGD is commonly used for distributed training ...
11/27/2018 ∙ by Mahmoud Assran, et al. ∙ 0 ∙ share

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Provably Accelerated Randomized Gossip Algorithms

In this work we present novel provably accelerated gossip algorithms for...
10/31/2018 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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Accelerated Gossip via Stochastic Heavy Ball Method

In this paper we show how the stochastic heavy ball method (SHB) -- a po...
09/23/2018 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

In this paper we study several classes of stochastic optimization algori...
12/27/2017 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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Linearly convergent stochastic heavy ball method for minimizing generalization error

In this work we establish the first linear convergence result for the st...
10/30/2017 ∙ by Nicolas Loizou, et al. ∙ 0 ∙ share

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