arXiv research

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

Model quantifies uncertainty's impact on European option prices.

Quantum methods model uncertain volatility in financial markets.

Integrates estimation and optimization for uncertain parameters.

This paper improves learning uncertain Bayesian networks from incomplete data.

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 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…

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

Adapts MBDOE for real-time parameter estimation in complex systems.

In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …

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

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

Proposes a RL method using simulators for stabilizing uncertain systems.

HNPE uses auxiliary data to estimate parameters in uncertain models.

We use copulas to improve SLAM in uncertain environments.

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

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

Flexible framework integrates machine learning and DRO for uncertain parameter prediction.

New method combines ODE filters and numerical quadrature to propagate model uncertainty.

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

New method calculates Shapley values for uncertain functions.

New loss function handles uncertain constraints in CSLO problems.

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…

Considering mean-variance portfolio problems with uncertain model parameters, we contrast the classical absolute robust optimization approach with the relative robust approach based on a maximum regret function. Although the latter problems are NP-hard in general, we show that tractable inner and outer approximations e…

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

GP-MRO discovers robust mixed strategies for unknown objectives.

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 …

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…

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

Federated learning (FL) is a distributed learning approach where a set of end-user devices participate in the learning process by acting on their isolated local data sets. Here, we process local data sets of users where worst-case optimization theory is used to reformulate the FL problem where the impact of local data …

New algorithm for uncertain time series classification.

With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear depen…

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.

Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. Th…

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

We study the Markowitz portfolio selection problem with unknown drift vector in the multidimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering theory is used to learn the posterior distribution about the drift …

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

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

We study the point of transition between complete and incomplete financial models thanks to Dirichlet Forms methods. We apply recent techniques, developped by Bouleau, to hedging procedures in order to perturbate parameters and stochastic processes, in the case of a volatility parameter fixed but uncertain for traders;…

Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.

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

In this paper we consider stochastic optimization problems for an ambiguity averse decision maker who is uncertain about the parameters of the underlying process. In a first part we consider problems of optimal stopping under drift ambiguity for one-dimensional diffusion processes. Analogously to the case of ordinary o…

A new method for estimating uncertainties in neural ODEs without numerical integration.

PLoM learns stochastic solutions to PDEs with limited data.

This paper tackles JSSP with uncertain task durations using DRL.