arXiv research

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

The paper tackles robust control with uncertain dependence using data-driven methods.

This paper improves learning uncertain Bayesian networks from incomplete data.

New algorithm for uncertain time series classification.

Unified framework denoises data and abstains from uncertain predictions.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

Paper proposes online optimization for uncertain systems using machine learning and DRO.

Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…

uMoE trains NNs with uncertain data by embedding uncertainty into training.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

We use copulas to improve SLAM in uncertain environments.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

A machine learning approach to compute Black-Scholes prices with uncertain volatility.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

Paper introduces imprecise logistic regression for handling uncertain data.

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…

Study on learning strategies in matching markets with uncertain preferences.

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In …

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep r…

Extends partitioned local depth concept with probabilistic considerations.

Model quantifies uncertainty's impact on European option prices.

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

Real-world problems typically require the simultaneous optimization of several, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization. …

New method recovers signals from noisy indirect data, even when noise is uncertain.

Federated learning (FL) is a distributed learning approach where a set of end-user devices participate in the learning process by acting on their isolated local data sets. Here, we process local data sets of users where worst-case optimization theory is used to reformulate the FL problem where the impact of local data …

This paper tackles JSSP with uncertain task durations using DRL.

Integrates estimation and optimization for uncertain parameters.

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

Proposes a new uncertain volatility model with worst-case scenario analysis.

New method optimizes resource allocation for uncertain tasks.

Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cy…

In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between …

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.

AIRBO optimizes robustly under uncertain inputs.

Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.

Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.

The study evaluates different probability models for uncertainty visualization using entropy calculations.

AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.

The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.

Novel algorithms for online learning with uncertain feedback graphs reduce regret.