If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim this inconsistency results from the unclear requirements the community e...
Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce...
Uncertainty may be taken to characterize inferences, their conclusions, their premises or all three. Under some treatments of uncertainty, the inferences itself is never characterized by uncertainty. We explore both the significance of uncertainty...
I propose a novel method, the Wasserstein Index Generation model (WIG), to generate a public sentiment index automatically. To test the model`s effectiveness, an application to generate Economic Policy Uncertainty (EPU) index is showcased....
We investigate the allocation of a co-owned company to a single owner using the Texas Shoot-Out mechanism with private valuations. We identify Knightian Uncertainty about the peer's distribution as a reason for its deterrent effect of a premature dis...
With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market...
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities. However, probabilistic computation presents significant challenges for most c...
Used to indicate uncertainty or possibility: We should maybe take a different route. Maybe it won't rain. n. Informal. 1. An uncertainty: There are so many maybes involved in playing the stock market. 2. An …
Price uncertainty in food commodities can create uncertainty for farmers and potentially negatively impact the level of farmer household well-being. On the other hand, the agriculture sector in the province of East Java has greatly contributed to Eas...
Deployed language models must decide not only what to answer but also when not to answer. We present UniCR, a unified framework that turns heterogeneous uncertainty evidence including sequence likelihoods, self-consistency dispersion, retrieval compa...
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain sam...
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the TrackML open dataset. Using the Monte Carlo Dropout method, we measure the...
In practice, one must recognize the inevitable incompleteness of information while making decisions. In this paper, we consider the optimal redeeming problem of stock loans under a state of incomplete information presented by the uncertainty in the (...
Mr.John has the right of this project,this is just a tempt to make some stimulation on autonomous vehicles motion planning.This is only for academic use. (⭐ 5)
[Abridged] We use our most recent training set for the RICO code to estimate the impact of recombination uncertainties on the posterior probability distributions which will be obtained from future CMB experiments, and in particular the Planck satel...
A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system r...
This is the Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence, which was held in Windsor, ON, August 18-20, 1989...
This is the Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, which was held in Cambridge, MA, Jul 27 - Jul 29, 1990...
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network. It employs a graph reconciliation and condensation process to...
In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen, Schollmeyer and Augustin...