arXiv research

The paper develops robust risk measures for uncertain loss positions.

This paper calculates worst-case target semi-variances for uncertain losses.

Flat solutions don't guarantee generalization for logistic loss in neural networks.

New loss function handles uncertain constraints in CSLO problems.

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

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

Unified framework denoises data and abstains from uncertain predictions.

New machine learning paradigm ignores loss function until action time.

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…

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

A new method for estimating uncertainties in neural ODEs without numerical integration.

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

A robust multiclass SVM tackles imbalanced data uncertainty.

A new method for decision-focused learning reduces computational cost.

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

New method calculates Shapley values for uncertain functions.

Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains. We design a new training algorithm, which is robust to missing or ambiguous labels. The main idea is to intentionally corrupt the labels of generated examples t…

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 develop a model for contagion in reinsurance networks by which primary insurers' losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature--namely proportional reinsurance--greatly underesti…

This work examines uncertainty sampling in binary classification using equivalent loss.

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

New method improves deep learning models in noisy label classification.

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.

In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to determine a calibration function. We demonstrate how applying certain transforma…

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

Optimizes risk sharing with multiple models under uncertainty.

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 Gaussian process models handle uncertain data locations in PDE approximations.

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

Improved DPO framework penalizes preference uncertainty to avoid overoptimization.

Uniform AMMs control loss in prediction markets.

Model quantifies uncertainty's impact on European option prices.

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

Efficient algorithm for evaluating hierarchical classification methods at multiple operating points.

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that underva…

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

Study optimal reinsurance pricing under model uncertainty for multiple insurers.

Algorithm predicts with optimal loss by abstaining from uncertain test examples.

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.

Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build mod…

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 …