arXiv research

Two-step conformal prediction method for adaptive bounding box uncertainties in multi-object detection.

Paper improves uncertainty quantification in PINNs using error bounds and solution bundles.

New rigorous uncertainty bounds for Gaussian Process regression.

Improved asset pricing using uncertainty-adjusted sorting in machine learning models.

Efficient classifier with uncertainty bounds for safety-critical applications.

BCPO optimizes offline RL policies by converting uncertainty into conservative bounds.

The paper bounds solutions to complex optimization problems with uncertain data.

The paper proves uncertainty principles on Finsler measure spaces.

New bounds on adaptivity cost in stochastic optimization.

This work introduces a method to decompose uncertainty in in-context learning for large language models.

New bounds for kernel regression under non-Gaussian noise.

Paper develops a new RL method for MDPs with uncertainty, achieving better regret bounds.

The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.

We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to …

TULiP estimates uncertainty for deep learning models safely.

Paper improves SLCB regret bound for bounded noise.

Motivated by applications in model-free finance and quantitative risk management, we consider Fréchet classes of multivariate distribution functions where additional information on the joint distribution is assumed, while uncertainty in the marginals is also possible. We derive optimal transport duality results for the…

Method quantifies uncertainties in complex MRF models.

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confi…

New algorithm reduces regret in RL with adversarial corruption.

We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricti…

New framework identifies and reduces errors in machine learning under distribution shift.

A new method optimizes robustness measures under input uncertainty using randomized Gaussian process upper confidence bound.

The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to …

Paper introduces CRP-O framework for uncertainty quantification in deep operators.

The paper evaluates joint life insurance risk under dependence uncertainty using copulas and convex risk measures.

New framework improves model reliability under distribution shifts.

New method certifies neural network robustness to random input noise.

The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.

Novel MOBO method for risk measures under input uncertainty.

Proposes new priors for neural networks to improve generalization and uncertainty.

A new Thompson Sampling framework handles uncertainty by imputing missing data.

INNs produce interval-valued uncertainty scores for DNNs.

PROWL uses robust reward estimates to improve ITR selection.

Concentration of infinitely exchangeable sequences with bounded-difference constants

Sharp bounds for distortion risk metrics under uncertain distributions.

Bayesian regression underestimates parameter uncertainties in noisy models.

Paper quantifies distortion risk measures' robustness to distributional uncertainty.

We extend Bayes' theorem for upper probabilities considering likelihood uncertainty.

New method quantifies uncertainty in reinforcement learning models.

We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn to achieve a challenging control goal (e.g., an agile maneuver close to a boundary). A central challenge in this setting is how to quantify…

Bayesian meta learning improves uncertainty quantification in regression.

Study on coskewness under varying dependence uncertainty.

The problem of machine learning with missing values is common in many areas. A simple approach is to first construct a dataset without missing values simply by discarding instances with missing entries or by imputing a fixed value for each missing entry, and then train a prediction model with the new dataset. A drawbac…

Improves robust transfer learning with side information.

New method improves uncertainty quantification for large batch sizes and misspecified models.

Structured credal learning separates covariate shift and label disagreement.

Researchers find a way to price American options without relying on specific asset price models.