arXiv research

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

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

New model predicts dynamic volatility in uncertain financial markets.

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

We use copulas to improve SLAM in uncertain environments.

Model quantifies uncertainty's impact on European option prices.

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

Study robust utility maximization with uncertain continuous semimartingales.

The paper tackles robust control for insurance contracts under uncertain transition rates.

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

Noise can stabilize systemic risk models with uncertain robustness.

Unified framework denoises data and abstains from uncertain predictions.

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…

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

New method calculates Shapley values for uncertain functions.

Optimizes investment under uncertain time horizons with non-concave utility.

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…

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

Quantum methods model uncertain volatility in 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 …

Proposes a new method for predicting uncertain net electricity demand.

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

New algorithm for uncertain time series classification.

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

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

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

Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.

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

We introduce a multivariate diffusion model that is able to price derivative securities featuring multiple underlying assets. Each asset volatility smile is modeled according to a density-mixture dynamical model while the same property holds for the multivariate process of all assets, whose density is a mixture of mult…

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

We propose a physics-based method to learn environmental fields (EFs) using a mobile robot. Common purely data-driven methods require prohibitively many measurements to accurately learn such complex EFs. Alternatively, physics-based models provide global knowledge of EFs but require experimental validation, depend on u…

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 …

This paper improves learning uncertain Bayesian networks from incomplete data.

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

Safe learning in uncertain systems with state measurements and optimization.

New loss function handles uncertain constraints in CSLO problems.

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.

Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.

Proposes DenoiseBid to correct uncertain CTR and CVR estimates for autobidding.

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

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

This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.

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

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

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…