A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

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Publications associées (29)

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We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Association for the Advancement of Artificial Intelligence (AAAI)2023

The statistical complexity of early-stopped mirror descent

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Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

Oxford Univ Press2023

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The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

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espm: A Python library for the simulation of STEM-EDXS datasets

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We present two open-source Python packages: "electron spectro-microscopy"(espm) and "electron microscopy tables"(emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes ...

ELSEVIER2023

Localizing Unsynchronized Sensors With Unknown Sources

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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Hypergraph-Based Fast Distributed AC Power Flow Optimization

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IEEE2023

A Multiconfigurational Wave Function Implementation of the Frenkel Exciton Model for Molecular Aggregates

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We present an implementation of the Frenkel exciton model into the OpenMolcas program package enabling calculations of collective electronic excited states of molecular aggregates based on a multiconfigurational wave function description of the individual ...

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In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

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Data-Driven Control and Optimization under Noisy and Uncertain Conditions

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Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

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