Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Guedj Benjamin"'
The clearance of explosive remnants of war (ERW) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its efficiency and effectiveness. In particular, research on artificial intelligence
Externí odkaz:
http://arxiv.org/abs/2411.05813
Publikováno v:
Neurips 2024, Dec 2024, Vancouver, Canada
PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might not alway
Externí odkaz:
http://arxiv.org/abs/2410.10230
We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term electricity consumption in France, which is of strategic importance for en
Externí odkaz:
http://arxiv.org/abs/2409.05934
Publikováno v:
Bioresource Technology 2023
Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian procedure, called VarBUQ, ensuring
Externí odkaz:
http://arxiv.org/abs/2405.19824
Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations. The problem is well-known to be intractable for most application domains, except in notable cases such as the tabular
Externí odkaz:
http://arxiv.org/abs/2402.09796
Modern machine learning usually involves predictors in the overparametrised setting (number of trained parameters greater than dataset size), and their training yield not only good performances on training data, but also good generalisation capacity.
Externí odkaz:
http://arxiv.org/abs/2402.08508
This paper contains a recipe for deriving new PAC-Bayes generalisation bounds based on the $(f, \Gamma)$-divergence, and, in addition, presents PAC-Bayes generalisation bounds where we interpolate between a series of probability divergences (includin
Externí odkaz:
http://arxiv.org/abs/2402.05101
Autor:
Clerico, Eugenio, Guedj, Benjamin
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keeps the network in a lazy training regime, where the dynamics can be lin
Externí odkaz:
http://arxiv.org/abs/2312.13259
We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by releasing a local predictor but keeping secret its training dataset with respect to the other nodes.
Externí odkaz:
http://arxiv.org/abs/2310.11203
Autor:
Hellström, Fredrik, Guedj, Benjamin
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the training and population loss. The bounds hold under the assumption that the c
Externí odkaz:
http://arxiv.org/abs/2310.10534