Zobrazeno 1 - 10
of 4 250
pro vyhledávání: '"Salé, A."'
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improv
Externí odkaz:
http://arxiv.org/abs/2406.02354
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications. In this paper, we propose novel measures for the quantification of aleatoric and epistemic unce
Externí odkaz:
http://arxiv.org/abs/2404.12215
Autor:
Rodemann, Julian, Croppi, Federico, Arens, Philipp, Sale, Yusuf, Herbinger, Julia, Bischl, Bernd, Hüllermeier, Eyke, Augustin, Thomas, Walsh, Conor J., Casalicchio, Giuseppe
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certa
Externí odkaz:
http://arxiv.org/abs/2403.04629
Autor:
Qaisar, Muhammad Umar Farooq, Yuan, Weijie, Bellavista, Paolo, Han, Guangjie, Zakariyya, Rabiu Sale, Ahmed, Adeel
The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable senso
Externí odkaz:
http://arxiv.org/abs/2402.10873
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications. This paper proposes a novel way to use var
Externí odkaz:
http://arxiv.org/abs/2401.00276
In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order probability distr
Externí odkaz:
http://arxiv.org/abs/2312.00995
In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise. They also giv
Externí odkaz:
http://arxiv.org/abs/2307.06831
Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence. As an alternative to representing uncertainty via one single probability meas
Externí odkaz:
http://arxiv.org/abs/2306.09586
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or learning from partial labels, a variant of weakly supervised learning,
Externí odkaz:
http://arxiv.org/abs/2306.01191
Autor:
Civil, Rita, Brook, Matthew S., Santos, Lívia, Varley, Ian, Elliott-Sale, Kirsty J., Lensu, Sanna, Ahtiainen, Juha P., Kainulainen, Heikki, Koch, Lauren G., Britton, Steven L., Wilkinson, Daniel J., Smith, Kenneth, Atherton, Philip J., Sale, Craig
Publikováno v:
In Bone December 2024 189