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MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Publications associées (28)

Networked Signal and Information Processing: Learning by multiagent systems

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Rachid Guerraoui, Omid Ardakanian, Alexandre David Olivier Maurer

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for ...

MDPI2023

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Alternation makes the adversary weaker in two-player games

Volkan Cevher, Ali Kavis, Luca Viano, Efstratios Panteleimon Skoulakis

Motivated by alternating game-play in two-player games, we study an altenating variant of the Online Linear Optimization (OLO). In alternating OLO, a learner at each round t ∈[n] selects a vector xt and then an adversary selects a cost-vector ct ∈[−1,1]n. ...

2023

Efficacy of temporal and spatial abstraction for training accurate machine learning models: A case study in smart thermostats

Rachid Guerraoui, Omid Ardakanian, Alexandre David Olivier Maurer

Smart thermostats are increasingly popular in homes and buildings as they improve occupant comfort, lower energy use in heating and cooling systems, and reduce utility bills by automatically adjusting room temperature according to measurements of their bui ...

ELSEVIER SCIENCE SA2023

Text Representation Learning for Low Cost Natural Language Understanding

Jan Frederik Jonas Florian Mai

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

EPFL2023

DeepValve: Development and experimental testing of a Reinforcement Learning control framework for occupant-centric heating in offices

Dolaana Khovalyg, Amirreza Heidari

Space heating controls in offices usually follow static schedules detached from actual occupancy, which results in energy waste by unnecessarily heating vacant offices. The uniqueness of stochastic occupancy profile and thermal response time of each office ...

2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023