arXiv research

GWINs improve classifier accuracy by translating uncertain observations.

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

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

Quantum methods model uncertain volatility in financial markets.

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

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 …

New clustering method for uncertain data using Wasserstein barycenters.

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

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

This paper improves learning uncertain Bayesian networks from incomplete data.

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 …

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

Meta-learning model predicts intervention effects from uncertain causal graphs.

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

Enhances Bayesian model comparison with a probabilistic framework for meta-uncertainty.

We study the Markowitz portfolio selection problem with unknown drift vector in the multidimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering theory is used to learn the posterior distribution about the drift …

Optimizes decision-making with uncertain variables using auxiliary observations.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

HNPE uses auxiliary data to estimate parameters in uncertain models.

New method calculates Shapley values for uncertain functions.

Optimizes investment under uncertain time horizons with non-concave utility.

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

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distrib…

Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.

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…

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. O…

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

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.

Flexible framework integrates machine learning and DRO for uncertain parameter prediction.

A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.

Meta-learning method for estimating time-varying mHealth intervention effects.

New algorithm for uncertain time series classification.

A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.

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

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Me…

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…

Bayesian algorithm stabilizes unknown continuous-time systems from unstable data.

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.

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

Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture of uncertainty. We therefore use principled Bayesian methods to capture…

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

AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.

Model quantifies uncertainty's impact on European option prices.

We study a Markov-Functional (MF) interest-rate model with Uncertain Volatility Displaced Diffusion (UVDD) digital mapping, which is consistent with the volatility-smile phenomenon observed in the option market. We first check the impact of pricing Bermudan swaptions by the model. Next, we also investigate the future s…

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