Zobrazeno 1 - 10
of 13 341
pro vyhledávání: '"D'Antonio, G."'
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-wo
Externí odkaz:
http://arxiv.org/abs/2409.08760
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed th
Externí odkaz:
http://arxiv.org/abs/2409.08676
This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference methods that assume static graphs and generate point-wise estimates, ou
Externí odkaz:
http://arxiv.org/abs/2409.08238
We study the problem of computing deterministic optimal policies for constrained Markov decision processes (MDPs) with continuous state and action spaces, which are widely encountered in constrained dynamical systems. Designing deterministic policy g
Externí odkaz:
http://arxiv.org/abs/2408.10015
Autor:
Escudero-Arnanz, Óscar, Soguero-Ruiz, Cristina, Álvarez-Rodríguez, Joaquín, Marques, Antonio G.
Antimicrobial Resistance represents a significant challenge in the Intensive Care Unit (ICU), where patients are at heightened risk of Multidrug-Resistant (MDR) infections-pathogens resistant to multiple antimicrobial agents. This study introduces a
Externí odkaz:
http://arxiv.org/abs/2407.17165
Autor:
Bello-Morales, Antonio G., Jiménez, Jose Beltrán, Cano, Alejandro Jiménez, Maroto, Antonio L., Koivisto, Tomi S.
Theories formulated in the arena of teleparallel geometries are generically plagued by ghost-like instabilities or other pathologies that are ultimately caused by the breaking of some symmetries. In this work, we construct a class of ghost-free theor
Externí odkaz:
http://arxiv.org/abs/2406.19355
Autor:
Jiang, Liuyuan, Xiao, Quan, Tenorio, Victor M., Real-Rojas, Fernando, Marques, Antonio G., Chen, Tianyi
Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that
Externí odkaz:
http://arxiv.org/abs/2406.10148
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due to biases in data. Such discrimination is known to be exacerbated when
Externí odkaz:
http://arxiv.org/abs/2406.09513
Autor:
Rozada, Sergio, Marques, Antonio G.
The (efficient and parsimonious) decomposition of higher-order tensors is a fundamental problem with numerous applications in a variety of fields. Several methods have been proposed in the literature to that end, with the Tucker and PARAFAC decomposi
Externí odkaz:
http://arxiv.org/abs/2406.18560
Autor:
Rozada, Sergio, Marques, Antonio G.
The goal of reinforcement learning is estimating a policy that maps states to actions and maximizes the cumulative reward of a Markov Decision Process (MDP). This is oftentimes achieved by estimating first the optimal (reward) value function (VF) ass
Externí odkaz:
http://arxiv.org/abs/2405.17628