arXiv research

We use copulas to improve SLAM in uncertain environments.

This paper improves learning uncertain Bayesian networks from incomplete data.

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

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

This paper tackles JSSP with uncertain task durations using DRL.

In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of…

Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.

In this paper we propose a new methodology for solving an uncertain stochastic Markovian control problem in discrete time. We call the proposed methodology the adaptive robust control. We demonstrate that the uncertain control problem under consideration can be solved in terms of associated adaptive robust Bellman equa…

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

Novel algorithms for online learning with uncertain feedback graphs reduce regret.

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

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

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

New algorithm for uncertain time series classification.

Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Simil…

Paper proposes efficient method for estimating risk measures in complex models.

Unified framework denoises data and abstains from uncertain predictions.

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

New method calculates Shapley values for uncertain functions.

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

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…

A new clustering method handles uncertain covariates efficiently.

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

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 …

New methods improve uncertainty explanations for models.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

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

We employ the thermal optimal path method to explore both the long-term and short-term interaction patterns between the onshore CNY and offshore CNH exchange rates (2012-2015). For the daily data, the CNY and CNH exchange rates show a weak alternate lead-lag structure in most of the time periods. When CNY and CNH displ…

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

Paper introduces imprecise logistic regression for handling uncertain data.

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…

Study on learning strategies in matching markets with uncertain preferences.

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 …

Novel framework for risk-sensitive reinforcement learning using martingale decomposition.

New framework models time-uncertain point processes for better event prediction.

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

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep r…

UAPCA projects uncertain data to low dimensions using GMMs.

Extends partitioned local depth concept with probabilistic considerations.

Model quantifies uncertainty's impact on European option prices.

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

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. …

New method recovers signals from noisy indirect data, even when noise is uncertain.

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 …

Integrates estimation and optimization for uncertain parameters.