arXiv research

A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.

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

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…

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

Noise can stabilize systemic risk models with uncertain robustness.

Proposes $φ$-table for statistical SHAP explanations in regression models.

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

New method calculates Shapley values for uncertain functions.

In this paper, we propose an equilibrium pricing model in a dynamic multi-period stochastic framework with uncertain income streams. In an incomplete market, there exist two traded risky assets (e.g. stock/commodity and weather derivative) and a non-traded underlying (e.g. temperature). The risk preferences are of expo…

MLShrink integrates machine learning with wavelet shrinkage for denoising.

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

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…

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.

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

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.

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

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

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.

SIP framework discovers governing equations in uncertain systems.

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 …

This paper improves learning uncertain Bayesian networks from incomplete data.

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 …

Investor optimizes worst-case portfolio in uncertain markets.

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

AIRBO optimizes robustly under uncertain inputs.

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.

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

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…

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

Study on learning strategies in matching markets with uncertain preferences.

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…

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 application of compressive sensing (CS) to structural health monitoring is an emerging research topic. The basic idea in CS is to use a specially-designed wireless sensor to sample signals that are sparse in some basis (e.g. wavelet basis) directly in a compressed form, and then to reconstruct (decompress) these si…

Investor optimizes investment and consumption under uncertain market conditions with constraints.