Publications associées à Three- or Two-Stage Stochastic Market-Clearing Algorithm?

Publications associées (27)

An improved statistical wake meandering model

Fernando Porté Agel, Corey Dean Markfort, Peter Andreas Brugger

A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while ...

IOP Publishing2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Dispatch-aware Optimal Planning of Active Distribution Networks including Energy Storage Systems

Ji Hyun Yi

The thesis develops a planning framework for ADNs to achieve their dispatchability by means of ESS allocation while ensuring a reliable and secure operation of ADNs. Second, the framework is extended to include grid reinforcements and ESSs planning. Finall ...

EPFL2023

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2024

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Aswin Suresh

We study socio-political systems in representative democracies. Motivated by problems that affect the proper functioning of the system, we build computational methods to answer research questions regarding the phenomena occurring in them. For each phenomen ...

EPFL2023

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Alberto Antonietti, Davide Bella, Pranav Rai, Henry Markram, Felix Schürmann, Eilif Benjamin Muller, Michael Reimann, Marwan Muhammad Ahmed Abdellah, Werner Alfons Hilda Van Geit, Lida Kanari, Srikanth Ramaswamy, James Gonzalo King, Alexis Arnaudon, Ying Shi, András Ecker, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Michael Emiel Gevaert, Nadir Roman Guerrero, Liesbeth Maria L Vanherpe, Huanxiang Lu, Mohameth François Sy, Cristina Colangelo, Juan Bautista Hernando Vieites, Cyrille Pierre Henri Favreau, Genrich Ivaska, Anna-Kristin Kaufmann, Joni Henrikki Herttuainen, Elvis Boci, Vishal Sood, Thomas Brice Delemontex, Fabien Petitjean, Julian Martin Budd, Mustafa Kerem Kurban

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, i ...

2024

Wind-Topo_Model

Michael Lehning, Jérôme François Sylvain Dujardin

Model and Data: Dujardin, J.; Lehning, M. Wind-Topo_model. EnviDat 2022 ...

Zenodo2022

Reliable Dispatch of Active Distribution Networks via a Two-layer Grid-Aware Model Predictive Control: Theory and Experimental Validation

Mario Paolone, Rahul Kumar Gupta, Antonio Zecchino, Ji Hyun Yi

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stoch ...

2022

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Rachid Guerraoui, Le Nguyen Hoang, Sadegh Farhadkhani, Oscar Jean Olivier Villemaud

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes ...

PMLR2022

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Daniel Keller

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system work ...

2022

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Daniel Keller

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

HUMANA PRESS INC2022

Entropic Fokker-Planck kinetic model

Mohammadhossein Gorji

The diffusion limit of kinetic systems has been subject of numerous studies since prominent works of Lebowitz et al. [1] and van Kampen [2]. More recently, the topic has seen a fresh interest from the rarefied gas simulation perspective. In particular, Fok ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Exploring Spatial Perspective Taking in Human-Robot Interaction

Elmira Yadollahi

Humans often rely on their perspective taking skills to thrive within the world's complex relations and connections. An adequate understanding of others' spatial perspectives can increase the quality of the interaction, not only perceptually but also cogni ...

EPFL2021

Operational route choice methodologies for practical applications

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Evanthia Kazagli

This paper focuses on the application of tractable route choice models and presents a set of methods for deriving relevant disaggregate and aggregate route choice indicators, namely link and route flows. Tractability is achieved at the disaggregate level b ...

2020

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy

We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two terms: (i) a stocha ...

Microtome Publishing2020

Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Zhenzhu Meng

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to im ...

2020

Gaussian and sparse processes are limits of generalized Poisson processes

Michaël Unser, Julien René Fageot, Virginie Sophie Uhlmann

The theory of sparse stochastic processes offers a broad class of statistical models to study signals, far beyond the more classical class of Gaussian processes. In this framework, signals are represented as realizations of random processes that are soluti ...

2020

The sources of sovereign risk: a calibration based on Levy stochastic processes

Sylvain Jean Pascal Carré

Governments choose to issue risky or riskless debt depending on the nature of the stochastic process of output. We use Brownian motion and Poisson shocks a modeling method in the literature on corporate default known as Levy processes to approximate a deco ...

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