arXiv research

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

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

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…

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

New method optimizes resource allocation for uncertain tasks.

Complexity science offers new insights into macroeconomics and finance.

The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time…

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

New method calculates Shapley values for uncertain functions.

The paper identifies when larger models improve predictions and proposes a switcher model.

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 algorithm solves uncertain Markov decision processes using Wasserstein uncertainty.

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

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 …

Paper speeds up visualization of uncertain data.

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

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

Improved drug-protein interaction prediction using FTL method.

New algorithm for uncertain time series classification.

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

Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to bette…

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.

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.

Framework for imputing time series data with uncertainty measures.

Single neuron learns predictive uncertainty in deep learning models.

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 …

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.

There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …