arXiv research

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

New model predicts dynamic volatility in uncertain financial markets.

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…

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

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 …

Quantum methods model uncertain volatility in financial markets.

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

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

New method calculates Shapley values for uncertain functions.

Model quantifies uncertainty's impact on European option prices.

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

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

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 …

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

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

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

New algorithm for uncertain time series classification.

We use copulas to improve SLAM in uncertain environments.

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…

In this paper, we study the asymptotic behavior of Asian option prices in the worst case scenario under an uncertain volatility model. We give a procedure to approximate the Asian option prices with a small volatility interval. By imposing additional conditions on the boundary condition and cutting the obtained Black-S…

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

AIRBO optimizes robustly under uncertain inputs.

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…

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

Solves super-hedging for financial models with uncertain prices.

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

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

We study an optimal execution problem with uncertain market impact to derive a more realistic market model. We construct a discrete-time model as a value function for optimal execution. Market impact is formulated as the product of a deterministic part increasing with execution volume and a positive stochastic noise pa…

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

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

This paper studies the properties of the optimal portfolio-consumption strategies in a {finite horizon} robust utility maximization framework with different borrowing and lending rates. In particular, we allow for constraints on both investment and consumption strategies, and model uncertainty on both drift and volatil…

In the present paper, given an evolving mixture of probability densities, we define a candidate diffusion process whose marginal law follows the same evolution. We derive as a particular case a stochastic differential equation (SDE) admitting a unique strong solution and whose density evolves as a mixture of Gaussian d…

Study robust utility maximization with uncertain continuous semimartingales.

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

Integrates estimation and optimization for uncertain parameters.

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 …

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

This paper tackles JSSP with uncertain task durations using DRL.

SOLBP extends efficient inference to uncertain Bayesian networks.

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…

TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.

DRO optimizes decisions under uncertain distributions, considering worst-case scenarios.

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.

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…

New algorithm minimizes worst-case regret in uncertain, time-varying dynamics.

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

This paper analyzes the robust growth rate of leveraged ETFs under uncertain parameters.