arXiv research

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

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

Deep RL agent improves lane changing in unpredictable traffic.

Supply uncertainty leads to inefficient supply chain network formation.

We study the risk assessment of uncertain cash flows in terms of dynamic convex risk measures for processes as introduced in Cheridito, Delbaen, and Kupper (2006). These risk measures take into account not only the amounts but also the timing of a cash flow. We discuss their robust representation in terms of suitably p…

New clustering method for uncertain data using Wasserstein barycenters.

New method designs fairer transport plans with uncertainty.

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

New method calculates Shapley values for uncertain functions.

A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.

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…

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

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.

A new clustering method handles uncertain covariates efficiently.

New algorithm for uncertain time series classification.

HNPE uses auxiliary data to estimate parameters in uncertain models.

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

AntLer anticipates future learning to improve control performance.

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

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

Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.

A new method solves complex financial equations efficiently.

A new policy learning method allows policies to abstain when uncertain, improving safety and applicability.

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.

ProBoost boosts probabilistic classifiers by focusing on uncertain samples.

Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.

This paper tackles JSSP with uncertain task durations using DRL.

This paper improves learning uncertain Bayesian networks from incomplete data.

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.

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.

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic desc…

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

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

In this paper, we present a unified framework for decision making under uncertainty. Our framework is based on the composite of two risk measures, where the inner risk measure accounts for the risk of decision given the exact distribution of uncertain model parameters, and the outer risk measure quantifies the risk tha…

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

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