arXiv research

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.

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…

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

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

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

Framework for confidence estimation in deep CT reconstructions.

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

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

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

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…

DRF improves confidence and uncertainty assessment for multivariate conditional distributions.

We use PDPs with confidence bands to explain HPO results.

AUCRSS detects change points in partially observed multivariate autocorrelated data.

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…

Optimizes private statistics with noisy methods.

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…

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.

Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and…

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

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

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…

Clarifies the confidence interval approach for bioequivalence testing.

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

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

This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.

The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to e…

The paper provides a method to find optimal machine learning model parameters with confidence.

Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distribution (OOD) samples is very important to avoid classification errors. In the context of OOD detection for image classification, one of the recen…

NCP uses neural networks to efficiently learn conditional distributions.

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 …

DeepLR constructs confidence intervals for neural networks with asymmetric expansions.

The paper creates nonparametric confidence bands for band-limited functions.

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…

An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selec…