arXiv research

Proposes a new framework for deep learning conditional mean estimation with confidence regions.

DRF improves confidence and uncertainty assessment for multivariate conditional distributions.

Framework for confidence estimation in deep CT reconstructions.

Waldo method constructs valid confidence regions for simulator-based inference.

The paper develops methods for constructing confidence regions for regression functions in binary classification.

Method constructs confidence regions for linear models with arbitrary predictors.

Construction of tight confidence regions and intervals is central to statistical inference and decision making. This paper develops new theory showing minimum average volume confidence regions for categorical data. More precisely, consider an empirical distribution $\widehat{\boldsymbol{p}}$ generated from $n$ iid real…

Introduces CCR for constructing confidence regions from conformal predictions.

NCP uses neural networks to efficiently learn conditional distributions.

Constructs non-asymptotic confidence regions for unknown functions in RKHS.

The SPS method constructs confidence regions for true parameters with optimal sample complexity.

Develops an efficient approximation for full conformal prediction regions.

General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed descripti…

Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set---the set of plausible transition probabilities---which is usually constructed as a multi-dimensional confidence region. E…

The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.

A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum …

Proposes a method to accelerate safe sequential learning using offline data.

A new method, Residual-Permuted Sums, improves confidence region construction for linear regression models.

Spatially weighted conformal prediction improves uncertainty quantification in house price models.

ACORE improves hypothesis testing and confidence sets in likelihood-free inference.

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess t…

Paper develops methods for PCA inference with missing data and heteroskedastic noise.

DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.

Paper develops inference methods for low-rank tensors without debiasing.

PaRCE estimates model confidence for CNNs across various uncertainties.

In this paper, we study a simple algorithm to construct asymptotically valid confidence regions for model parameters using the batch means method. The main idea is to cancel out the covariance matrix which is hard/costly to estimate. In the process of developing the algorithm, we establish process-level functional cent…

We use PDPs with confidence bands to explain HPO results.

AUCRSS detects change points in partially observed multivariate autocorrelated data.

Proposes CPO framework for robust decision-making with explainable uncertainty regions.

We propose a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations. We construct an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale…

Wasserstein distributionally robust optimization estimators are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance (in a Wasserstein sense) from the underlying empirical measure. While motivated by…

Optimizes private statistics with noisy methods.

Biondi et al. (2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence c…

The paper improves confidence ellipsoids for ridge regression with PAC bounds.

Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on specially designed measurements and the corresponding noisy outcomes. In the last decade, numerous statistical methodologies have been developed for efficiently recovering the unknown low-rank matrices. However, in some applicat…

This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the standard approximations based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for conf…

Trust is a collective, self-fulfilling phenomenon that suggests analogies with phase transitions. We introduce a stylized model for the build-up and collapse of trust in networks, which generically displays a first order transition. The basic assumption of our model is that whereas trust begets trust, panic also begets…

Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure t…

FreB protocol uses AI to infer hidden parameters with valid confidence regions.

BALLET filters a high-confidence region of interest for Bayesian optimization.

Improved AI lung ultrasound segmentation using expert confidence values.

A new method uses SVM classification to efficiently compute confidence sets.

This paper analyzes the sample complexity of SPS method for scalar linear regression.

The research proposes a stopping rule for reinforcement learning algorithms based on instance-dependent confidence.

New rule reduces exploration regret to logarithmic, improving bad episode handling.

Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. Although the Gaussian process assumption implies a random distribution of the Bayesian optimization outputs, quantification of this uncertain…

We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters. We consider both hypothesis tests and confidence regions for generic penalized M-estimators. U…

Study on inventory management under uncertainty using smooth ambiguity preference.