Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Plassier, Vincent"'
We develop a new method for creating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution $P_{Y \mid X}$. Most existing methods, such as conformalized quantile regression and probabilisti
Externí odkaz:
http://arxiv.org/abs/2407.01794
Autor:
Plassier, Vincent, Kotelevskii, Nikita, Rubashevskii, Aleksandr, Noskov, Fedor, Velikanov, Maksim, Fishkov, Alexander, Horvath, Samuel, Takac, Martin, Moulines, Eric, Panov, Maxim
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on data exchangeability, a condition often violated in pr
Externí odkaz:
http://arxiv.org/abs/2312.15799
Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addresse
Externí odkaz:
http://arxiv.org/abs/2306.05131
This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations of FL such as communication bottlenecks and statistical heterogeneity.
Externí odkaz:
http://arxiv.org/abs/2211.00100
The raise of machine learning and deep learning led to significant improvement in several domains. This change is supported by both the dramatic rise in computation power and the collection of large datasets. Such massive datasets often include perso
Externí odkaz:
http://arxiv.org/abs/2207.13572
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their design to handle distr
Externí odkaz:
http://arxiv.org/abs/2106.06300
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients. FL raises many constraints which include privacy and data ownership, communication overhead, statis
Externí odkaz:
http://arxiv.org/abs/2106.00797
Consider the problem of learning a large number of response functions simultaneously based on the same input variables. The training data consist of a single independent random sample of the input variables drawn from a common distribution together w
Externí odkaz:
http://arxiv.org/abs/2006.09223
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.