arXiv research

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

We use copulas to improve SLAM in uncertain environments.

Bayesian method optimizes uncertain constraints in black-box function optimization.

This paper extends transfer learning for linear regression to uncertain domain information.

Proposes a new method for efficient model reconstruction with uncertain parameters.

This paper improves learning uncertain Bayesian networks from incomplete data.

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

AIRBO optimizes robustly under uncertain inputs.

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain paramet…

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

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Me…

Paper introduces imprecise logistic regression for handling uncertain data.

Extends partitioned local depth concept with probabilistic considerations.

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. …

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

Optimizes decision-making with uncertain variables using auxiliary observations.

Study examines strategic exit timing in uncertain competition.

New method calculates Shapley values for uncertain functions.

Paper uses ML for high-dimensional option pricing under uncertain volatility model.

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…

New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.

A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties. The main applications of the StackedGP framework are to integrate different datasets through model composition, enhance predictions of quantities of interest thr…

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 …

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

New algorithm for uncertain time series classification.

The paper tackles online resource allocation with uncertain coefficients and chance constraints.

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

Meta-learning model predicts intervention effects from uncertain causal graphs.

Paper automates variable selection for network anomaly detection.

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.

Bayesian optimization tackles uncertainty in context variables.

GWINs improve classifier accuracy by translating uncertain observations.

Model quantifies uncertainty's impact on European option prices.

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

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

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

This paper tackles JSSP with uncertain task durations using DRL.

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 …

Adapts Bayesian optimization for uncertain outcomes using stochastic sampling.

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

Federated learning uses worst-case optimization to handle uncertain local data impacts.

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that underva…

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 …

Geometrically convex return risk measures on AM-algebras