arXiv research

Quantum methods model uncertain volatility in financial markets.

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

New algorithm for uncertain time series classification.

Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …

Paper proposes efficient method for estimating risk measures in complex models.

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…

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

The paper develops robust risk measures for uncertain loss positions.

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…

Solves super-hedging for financial models with uncertain prices.

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

New clustering method for uncertain data using Wasserstein barycenters.

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 …

This paper proposes a general active learning framework to improve classifier performance.

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

The globalization feeded by the technology explosion that begans in the end of the last century, started the world to change faster every day. The only today's certain is the tomorrow's uncertain. Risk is defined as uncertain where one or many causes composed of ocurrence probality can generate an impact or consequence…

Improves prediction accuracy in document classification by measuring uncertainty.

Develops a risk-sensitive reinforcement learning framework for uncertain environments.

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

Safe learning in uncertain systems with state measurements and optimization.

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

New method calculates Shapley values for uncertain functions.

The Surprise index assesses autonomous systems' competency in uncertain environments.

The paper studies robust risk measures with linear penalties under uncertain distributions.

A robot learns environmental fields using physics-based models and Bayesian methods.

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 method measures model risk in dynamic settings with uncertain state processes.

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 …

Proposes MEDIC to improve incremental learning by preventing forgetting and intransigence.

AntLer anticipates future learning to improve control performance.

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

The framework of this paper is that of risk measuring under uncertainty, which is when no reference probability measure is given. To every regular convex risk measure on ${\cal C}_b(Ω)$, we associate a unique equivalence class of probability measures on Borel sets, characterizing the riskless non positive elements of $…

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…

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

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 method optimizes portfolio and hedging under uncertain market parameters.

Geometrically convex return risk measures on AM-algebras

This paper optimizes portfolio rebalancing under uncertain security returns using meta-heuristic algorithms.

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.

This paper examines how data affects risk measures in uncertain distributions.

Bayesian optimisation tackles uncertain inputs in noisy function evaluations.

Novel convex risk measures aggregate multiple uncertain sources for insurance firms.

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

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

GWINs improve classifier accuracy by translating uncertain observations.

Model quantifies uncertainty's impact on European option prices.