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On the Influence of Bias-Correction on Distributed Stochastic Optimization

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Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition tak ...

EPFL2019

Decentralized Consensus Optimization With Asynchrony and Delays

Ali H. Sayed, Kun Yuan, Tianyu Wu

We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network in which the agents communicate with their neighbors and perform local computation. In the proposed algorithm, each agent can compute and comm ...

2018

Asynchronous updates for stochastic gradient descent

Alberto Silvio Chiappa

Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...

2017

Decentralized exact coupled optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario. In this formulation, while the network performance is dependent on a collecti ...

2017

Generalised zonal modes in stationary axisymmetric plasmas

Jonathan Graves

The MHD model enables derivation and analysis of the rich structure of geodesic acoustic modes (GAMs) and zonal modes in axisymmetric magnetic confined plasmas. The modes are identifiable from a single dispersion relation as two branches of slow magnetoson ...

IoP2017

Decentralized consensus optimization with asynchrony and delays

Ali H. Sayed, Kun Yuan, Tianyu Wu

We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network of agents, where the agents perform local computation and communicate with neighbors. We design the algorithm so that the agents can compute a ...

IEEE2016

Inversion Minimization in Majority-Inverter Graphs

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù, Eleonora Testa

In this paper, we present new optimization techniques for the recently introduced Majority-Inverter Graph (MIG). Our optimizations exploit intrinsic algebraic properties of MIGs and aim at rewriting the complemented edges of the graph without changi ...

2016

Splitting Methods for Distributed Optimization and Control

Ye Pu

This thesis contributes towards the design and analysis of fast and distributed optimization algorithms based on splitting techniques, such as proximal gradient methods or alternation minimization algorithms, with the application of solving model predictiv ...

EPFL2016

Adaptive data augmentation for image classification

Pascal Frossard, Alhussein Fawzi

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations ...

IEEE2016

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016