A method for learning transition models in uncertain domains using relational rules and neural networks.
problem Learning transition models in complex, uncertain domains.
method Relational rules, greedy algorithm, neural networks.
result The method is more versatile and sample efficient than monolithic models.
New algorithm for uncertain time series classification.
problem Uncertainty in time series data.
method Uncertain dissimilarity measure based on Euclidean distance and uncertain shapelet transform.
result Effectiveness of the uncertain shapelet transform algorithm on state-of-the-art datasets.
This paper extends transfer learning for linear regression to uncertain domain information.
problem Transfer learning for linear regression with uncertain domain information.
method A Dirichlet process is used to infer latent domain information from regression coefficients. A novel framework considers the joint distribution of variables.
result The proposed method controls bias better than previous pseudo-labelling approaches.
Adapts Bayesian optimization for uncertain outcomes using stochastic sampling.
problem Optimizing with uncertain or stochastic outcomes in scientific and engineering problems.
method Proposes SSBO, a new framework that handles uncertainty and myopic decision making.
result SSBO techniques effectively optimize standard and applied problems.
uMoE trains NNs with uncertain data by embedding uncertainty into training.
problem Managing aleatoric uncertainty in NN-based predictive models.
method Divide and Conquer strategy, Expert components, Gating Unit.
result uMoE outperforms baseline methods in uncertainty management.
New algorithm improves GAN performance with minimal labels.
problem Improving GAN performance with little supervision.
method Intentionally corrupts generated labels to match real data statistics, trains discriminator with corrupted labels.
result Minimizing proposed loss is equivalent to minimizing true divergence between real and generated data.
Meta-learning model predicts intervention effects from uncertain causal graphs.
problem Estimating intervention effects when causal structures are uncertain.
method Model-Averaged Causal Estimation Transformer Neural Process (MACE-TNP) using meta-learning.
result MACE-TNP outperforms Bayesian baselines in predicting intervention distributions.
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…
The paper explores how to handle uncertain evidence in probabilistic models.
problem Handling uncertain evidence in probabilistic models and stochastic simulators.
method The paper considers distributional evidence, Jeffrey's rule, and virtual evidence as methods for interpreting uncertain evidence.
result The paper provides guidelines on how to account for uncertain evidence and highlights the importance of careful consideration.
New method calculates Shapley values for uncertain functions.
problem Uncertain value functions in explainable machine learning.
method Definition of Shapley values using probability theory.
result Shapley values can be applied to uncertain functions.
New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.
problem Policy evaluation under unobserved confounders in uncertain causal environments.
method Developed worst-case bounds for sensitivity to unobserved confounders, demonstrating model-based methods are more effective.
result Model-based approaches with robust MDPs provide sharper lower bounds for policy evaluation.
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.
problem Modeling financial asset prices with uncertain volatility.
method Quantum stochastic calculus with unitary and non-unitary time evolution.
result Different volatility levels encoded in quantum states, leading to varied market price evolutions.
New model predicts dynamic volatility in uncertain financial markets.
problem Predicting dynamic volatility in financial markets with uncertainty.
method Generalized Barndorff-Nielsen and Shephard (BN-S) model considering delay and fuzziness.
result Effective prediction of dynamic volatility with improved performance.
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.
problem Uncertain labeling in semi-supervised classification.
method Gaussian mixture model, Bayes risk computation, comparison with algorithm performance.
result New insights into semi-supervised learning algorithm performance.
Deep RL methods improve resource allocation in uncertain environments.
problem Optimizing resource allocation in dynamic, uncertain environments.
method Developed three DDPG-based approaches to handle constraints and combinatorial action spaces.
result Demonstrated improved performance over existing methods on real and semi-real data.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
problem Safety in reinforcement learning in unknown and uncertain environments.
method Growing-Window estimator sampling and Stochastic Pessimistic-Optimistic Thresholding (SPOT) algorithm.
result Achieves sublinear regret and constraint violation of i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) . This paper optimizes portfolio rebalancing under uncertain security returns using meta-heuristic algorithms.
problem Optimizing portfolio rebalancing under uncertain security returns with transaction costs.
method Meta-heuristic algorithms (genetic algorithm) for solving the portfolio rebalancing problem.
result Meta-heuristic algorithms provide better results than global optimization solvers for portfolio rebalancing under uncertainty.
A method for accurate pricing of multidimensional derivatives under uncertain volatility.
problem High-dimensional stochastic control problem in uncertain volatility model.
method Backward actor-critic stochastic policy gradient scheme combining DP, PPO, and neural networks.
result Accurate and efficient pricing of multidimensional derivatives compared to benchmarks.
The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.
problem Portfolio allocation with uncertain covariance matrices.
method Calculates the expected value of CARA utility function over a distribution of covariance matrices, considering uncertainty in future returns and covariances.
result Marginalization introduces a logarithmic dependence on risk, leading to lower allocation levels for higher uncertainties.
Bayesian optimisation tackles uncertain inputs in noisy function evaluations.
problem Optimizing functions with uncertain query locations and noisy outcomes.
method Proposes a UCB algorithm for BO with uncertain inputs, using Gaussian process models.
result Theoretical and experimental results show the proposed algorithm outperforms conventional methods.
Bayesian Gaussian process models handle uncertain data locations in PDE approximations.
problem Handling uncertainties in data locations for PDE approximations.
method Bayesian inference of uncertain inputs integrated into Gaussian process predictions.
result Substantial reduction in predictive uncertainties achieved through Bayesian inference.
Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.
problem Learning with label proportions (LLP) for uncertain data.
method Transfer learning approach to transfer knowledge from source to target tasks with uncertain data.
result The proposed TL-LLP method achieves better accuracy and is less sensitive to noise.
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
problem Non-cooperative games with globally uncertain parameters and no common prior.
method Mixed strategies and subjective priors, Extended Equilibrium defined by fixed-point argument.
result Existence of Extended Equilibrium under certain conditions.
GWINs improve classifier accuracy by translating uncertain observations.
problem Improving accuracy of uncertain observations in classifiers.
method Generative network recovers correct observation distributions, reject option allows for uncertain predictions.
result GWINs significantly improve classifier accuracy on benchmark datasets.
Model quantifies uncertainty's impact on European option prices.
problem Uncertainty in market volatility risk affects option pricing.
method Hamilton-Jacobi-Bellman framework and finite element method.
result Dependence of Delta on uncertainty is nonlinear and varied.
IDT learns human preferences from uncertain decisions, even when humans are suboptimal.
problem Learning human preferences from uncertain and suboptimal decisions.
method Inverse decision theory (IDT) framework, statistical analysis of IDT, characterizing sample complexity.
result Learning preferences is easier when decisions are more uncertain, even if humans are suboptimal.
The paper tackles robust control with uncertain dependence using data-driven methods.
problem Nonparametric robust control under dependence uncertainty in multi-period stochastic systems.
method Nonparametric adaptive robust control framework using stochastic gradient descent ascent algorithm.
result The controller benefits from knowing more about the uncertain model.
Paper proposes online optimization for uncertain systems using machine learning and DRO.
problem Optimization of uncertain dynamical systems with distributional uncertainty.
method Combines machine learning with Distributional Robust Optimization (DRO) to handle uncertainty.
result Online solutions with probabilistic regret bounds for uncertain systems.
Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.
problem Optimizing with uncertain objectives in multi-objective problems.
method Empirical approach to compare solutions with arbitrary distributions of uncertain objectives.
result Higher optimization quality achieved at lower overheads compared to existing techniques.
This paper tackles JSSP with uncertain task durations using DRL.
problem Job Shop Scheduling Problem with uncertain task durations.
method Integrates Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) to generate robust schedules.
result Advances DRL applications to JSSPs, enhancing generalization and scalability.
This paper improves learning uncertain Bayesian networks from incomplete data.
problem Learning conditional probabilities in Bayesian networks with limited data.
method Develops methods to estimate and quantify uncertainty in conditional probabilities with incomplete data.
result Improves state-of-the-art approaches for handling uncertain Bayesian networks with incomplete data.
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 …
Proposes a new uncertain volatility model with worst-case scenario analysis.
problem Modeling and pricing options under uncertain volatility.
method Connection between G-HJB equations and 2BSDEs for option pricing.
result Derives a limit model for worst-case price scenario.
Federated learning uses worst-case optimization to handle uncertain local data impacts.
problem Handling uncertainty in local data sets in federated learning.
method Reformulate FL problem using worst-case optimization theory, considering local data as uncertain functions bounded in a closed region.
result Comparison of FL performance with centralized learning and application of regularization factors.
This paper analyzes the multi-armed bandit problem using frequency-domain methods.
problem The exploration-exploitation trade-off in sequential decision-making.
method Proposes a frequency-domain analysis framework, reformulating the bandit process as a signal processing problem.
result Confidence bound term in UCB algorithm is equivalent to a time-varying gain in frequency domain.
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.
problem Lack of quantified predictive uncertainty in existing UnKGE methods.
method Proposes extsc{UnKGCP} framework using conformal prediction with a novel nonconformity measure.
result Sharp prediction intervals effectively capture predictive uncertainty in diverse UnKGE methods.
AIRBO optimizes robustly under uncertain inputs.
problem Challenges in Bayesian Optimization due to input uncertainty.
method AIRBO models arbitrary input uncertainty with MMD and accelerates posterior inference.
result AIRBO achieves state-of-the-art performance under various input uncertainties.
Deep RL agent improves lane changing in unpredictable traffic.
problem Uncertainty in other drivers' behaviors and safety vs agility trade-off.
method Developed a deep reinforcement learning agent in a simulated highway environment.
result Significantly better performance in noisy environments compared to heuristic methods.
Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.
problem Pricing equity-indexed annuities with early surrender risk under uncertain market conditions.
method Advanced financial modeling techniques, including uncertain volatility framework and Hull-White model for interest rate dynamics. Numerical algorithm using tree-based framework with local volatility optimization.
result High effectiveness of the proposed numerical algorithm compared to machine learning-based methods.
GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.
problem Uncertainty in atrial conduction velocity calculations.
method Gaussian Process Manifold Interpolation (GPMI) on human atrial manifolds.
result GPMI accounts for atrial topology and calculates CV uncertainty.
A machine learning approach to compute Black-Scholes prices with uncertain volatility.
problem Approximating financial markets with continuous-time models like Black-Scholes when data is discrete.
method Generalized Polynomial Chaos (gPC) method combined with a machine learning technique called Bi-Fidelity.
result Efficient numerical method to quantify uncertainty in derivative pricing.
The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.
problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.
method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.
result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.
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…
Novel algorithms for online learning with uncertain feedback graphs reduce regret.
problem Uncertainty in feedback graphs hinders traditional online learning approaches.
method Developed novel online learning algorithms to handle uncertain feedback graphs.
result Proved sublinear regret under mild conditions for the proposed algorithms.
Study on learning strategies in matching markets with uncertain preferences.
problem Decision-making in scarcity of shared resources with unknown agent preferences.
method Representation of preferences in a reproducing kernel Hilbert space, learning algorithm for uncertainty.
result Optimal strategies derived to maximize agents' expected payoffs, with stability and fairness properties.