arXiv research

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

Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.

The Surprise index assesses autonomous systems' competency in uncertain environments.

New algorithm minimizes worst-case regret in uncertain, time-varying dynamics.

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…

Paper uses evidence theory to improve stock price forecasting accuracy.

Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.

HRM-Agent learns to navigate dynamic mazes using reinforcement learning.

A new method improves uncertainty estimation in deep learning, especially for hard-to-label samples.

New method calculates Shapley values for uncertain functions.

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 …

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 …

A new framework for efficient sequence maps using Bayesian filtering and covariance.

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

New algorithm for uncertain time series classification.

AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing

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

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.

UA-LQE improves value function learning by selectively erasing uncertain entries in Q-matrix.

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

New method quantifies classifier uncertainty, revealing large variability in performance metrics.

Develops new methods for risk-aware decision-making in medical bandits.

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.

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

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

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. 'Active inference' is an emerging normative framework in cognitive and computational neuroscience that …

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.

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.

The paper tackles video prediction by estimating conditional densities implicitly.

Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.