arXiv research

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

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

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 …

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

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

This paper improves learning uncertain Bayesian networks from incomplete data.

Integrates estimation and optimization for uncertain parameters.

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

We use copulas to improve SLAM in uncertain environments.

Optimizes investment under uncertain time horizons with non-concave utility.

Real-world problems typically require the simultaneous optimization of several, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization. …

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

Unified framework denoises data and abstains from uncertain predictions.

This paper tackles JSSP with uncertain task durations using DRL.

The paper develops robust risk measures for uncertain loss positions.

This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the dist…

AIRBO optimizes robustly under uncertain inputs.

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

Meta-learning model predicts intervention effects from uncertain causal graphs.

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

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 …

SOLBP extends efficient inference to uncertain Bayesian networks.

New method calculates Shapley values for uncertain functions.

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…

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…

The study evaluates different probability models for uncertainty visualization using entropy calculations.

This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

Existing uncertainty modeling approaches try to detect an out-of-distribution point from the in-distribution dataset. We extend this argument to detect finer-grained uncertainty that distinguishes between (a). certain points, (b). uncertain points but within the data distribution, and (c). out-of-distribution points. O…

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

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the cl…

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

New algorithm for uncertain time series classification.

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 …

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

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

Paper develops online learning-based risk-averse MPC for uncertain systems.

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distrib…

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

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

Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.

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

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

Enhances Bayesian model comparison with a probabilistic framework for meta-uncertainty.

A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.

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 …

Model quantifies uncertainty's impact on European option prices.