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…
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48 results for “uncertain graphs”
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.
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guaranteesstat.ML
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.
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.
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.
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
SOLBP extends efficient inference to uncertain Bayesian networks.
problem Inference in uncertain Bayesian networks with second-order probabilities.
method Extends Loopy Belief Propagation to second-order Bayesian networks.
result Generates inferences consistent with sum-product networks, more efficient and scalable.
Agent learns to navigate uncertain 3D maps using a hybrid planner.
problem Planning in 3D environments with uncertain topological maps.
method Hierarchical strategy combining graph planner and local policy, data-driven learning with neural network.
result Machine learning can overcome missing information in probabilistic topological maps.
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 -BP algorithm improves belief propagation for graphs with loops.
problem Uncertainty in belief propagation for graphs with loops.
method Derive -BP algorithm motivated by minimizing -divergence.
result Proves convergence conditions for -BP.
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…
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cy…
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.
Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features);…
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.
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 .
Improved community detection and link prediction with GAE and VGAE.
problem Jointly improving community detection and link prediction with graph autoencoders.
method Introducing a community-preserving message passing scheme and doping encoders with Louvain-based prior communities.
result Empirical effectiveness of Modularity-Aware GAE and VGAE on various real-world graphs.
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 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.
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.
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.
Modularity-aware GAE and VGAE improve community detection and link prediction.
problem Improving community detection with GAE and VGAE in the absence of node features.
method Introducing a modularity-aware message passing scheme and regularizer to GAE and VGAE encoders.
result Jointly addressing community detection and link prediction with high accuracy is possible.
As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise …
Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systemseess.SY
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.
The paper tackles robust design selection for online experiments under uncertain interference mechanisms.
problem Designing experiments in ads, recommendations, and member-experience systems when interference mechanisms are unknown.
method Formulates the problem as robust design selection over uncertain exposure mechanisms. Compares designs by worst-case planning risk over an ambiguity set combining various factors.
result Develops a geometry-aware guarantee and robust selector theorem with excess-risk control, exact recovery under separation, and certified shortlists when the risk surface is flat.
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.
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.
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.
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 …
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.
Probabilistic fair clustering tackles uncertain group membership.
problem Fair clustering with imperfect group membership.
method Probabilistic algorithms for metric graphs and metric membership.
result Approximation ratio guarantees for fair clustering.
Robust Pricing of Equity-Indexed Annuities under Uncertain Volatility and Stochastic Interest Rateq-fin.PR
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.
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…
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
problem Predicting the movement of financial indices like S&P500.
method Transfer learning, causal graph, multidisciplinary knowledge, VAE network.
result 74.3% accuracy, 67% F1-score, 0.42 Matthew correlation on 12 years test period.
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.
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.
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…
In this paper, we study the asymptotic behavior of Asian option prices in the worst case scenario under an uncertain volatility model. We give a procedure to approximate the Asian option prices with a small volatility interval. By imposing additional conditions on the boundary condition and cutting the obtained Black-S…