arXiv research

Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.

Proposes a new method for predicting uncertain net electricity demand.

Deep RL methods improve resource allocation in uncertain environments.

Framework optimizes PV-battery investment timing to maximize value.

A new method uses GANs for robust optimization under uncertain data.

A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and…

Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connecti…

We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…

Optimizes decision-making with uncertain variables using auxiliary observations.

Optimal vehicle repositioning policy found for shared mobility services.

Supplier learns to price contracts against a learning retailer.

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

New method calculates Shapley values for uncertain functions.

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…

SPINN optimizes neural network inference on devices and cloud.

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 …

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

New algorithm for uncertain time series classification.

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

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios …

This paper optimizes portfolio rebalancing under uncertain security returns using meta-heuristic algorithms.

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 optimisation tackles uncertain inputs in noisy function evaluations.

RL enhances cloud autoscaling by learning optimal resource allocation.

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

Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.

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

GWINs improve classifier accuracy by translating uncertain observations.

Model quantifies uncertainty's impact on European option prices.

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

Investor optimizes portfolio under dynamic risk preferences.

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

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

Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.

This paper tackles JSSP with uncertain task durations using DRL.

This paper improves learning uncertain Bayesian networks from 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 …

Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.

Adapts Bayesian optimization for uncertain outcomes using stochastic sampling.

Proposes a new uncertain volatility model with worst-case scenario analysis.

Federated learning uses worst-case optimization to handle uncertain local data impacts.

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 …

This paper proposes a joint energy and data market to handle uncertainty in energy procurement.

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.

AIRBO optimizes robustly under uncertain inputs.