Publications associées à Convex block-sparse linear regression with expanders - provably

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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EPFL2024

Random matrix methods for high-dimensional machine learning models

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Graph generative deep learning models with an application to circuit topologies

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Elsevier Science Sa2024

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In this thesis we will present and analyze randomized algorithms for numerical linear algebra problems. An important theme in this thesis is randomized low-rank approximation. In particular, we will study randomized low-rank approximation of matrix functio ...

EPFL2024

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Efficient Continual Finite-Sum Minimization

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Reliable data-driven decision-making through optimal transport

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

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The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

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Spectral Estimators for High-Dimensional Matrix Inference

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A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

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Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

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Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

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Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

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In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

China Electric Power Research Inst2024

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

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How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

Association for Computing Machinery2024

Scalable constrained optimization

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Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

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Inhalation of Microplastics—A Toxicological Complexity

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Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the e ...

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On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography

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Ieee-Inst Electrical Electronics Engineers Inc2024

On the Generalization of Stochastic Gradient Descent with Momentum

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

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In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

Walter De Gruyter Gmbh2024