A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Responds to critiques on tests for causal parameter confidence intervals.
This paper improves GP-UCB by using a shifted exponential distribution for confidence parameters.
Method constructs confidence regions for linear models with arbitrary predictors.
The paper provides a method to find optimal machine learning model parameters with confidence.
ACORE improves hypothesis testing and confidence sets in likelihood-free inference.
This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.
Introduces CCR for constructing confidence regions from conformal predictions.
Waldo method constructs valid confidence regions for simulator-based inference.
Practical or scientific considerations often lead to selecting a subset of parameters as ``important.'' Inferences about those parameters often are based on the same data used to select them in the first place. That can make the reported uncertainties deceptively optimistic: confidence intervals that ignore selection g…
New method constructs confidence sets for GLMs via game theory.
A new method uses SVM classification to efficiently compute confidence sets.
Typical dimensionality reduction (DR) methods are often data-oriented, focusing on directly reducing the number of random variables (features) while retaining the maximal variations in the high-dimensional data. In unsupervised situations, one of the main limitations of these methods lies in their dependency on the sca…
We consider the setting of linear regression in high dimension. We focus on the problem of constructing adaptive and honest confidence sets for the sparse parameter θ, i.e. we want to construct a confidence set for theta that contains theta with high probability, and that is as small as possible. The l_2 diameter of a …
The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach relies on the Neyman construc…
Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data. In this paper, we consider the dimensionality reduction in parameter spaces of binary multivariate distributions. We propose a general Confident-Information-First (CIF) prin…
This paper extends the existing literature on empirical estimation of the confidence intervals associated to the Detrended Fluctuation Analysis (DFA). We used Montecarlo simulation to evaluate the confidence intervals. Varying the parameters in DFA technique, we point out the relationship between those and the standard…
The SPS method constructs confidence regions for true parameters with optimal sample complexity.
The study examines methods to correct measurement error in nutritional epidemiology studies.
Improved Bayesian optimisation method using randomised Gaussian process UCB.
Novel confidence sets improve linear bandit performance by adapting to unknown noise levels.
EB-TCε identifies the best arm with ε confidence in stochastic bandits.
A method to approximate instance-dependent label noise using instance-confidence embedding.
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 generated from iid real…
A new framework bridges classical and machine learning methods for reliable inference from complex models.
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…
Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples (). In order to make informative inference, we assume that the model is approximately sparse, that is th…
In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dim…
The paper shows over-confidence in models isn't just due to over-parametrization.
Paper certifies intersection of minimum-volume confidence sets for multinomial outcomes.
Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.
Private statistical inference methods improve confidence interval lengths.
New methods improve confidence set calibration in complex models.
CCAC calibrates DNN classifiers on OOD datasets by separating mis-classified samples.
The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the…
The paper introduces methods to quantify uncertainty in sampling without replacement.
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-c…
Improved confidence bounds for linear logistic model with applications to bandits.
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents…
This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean sq…
FreB protocol uses AI to infer hidden parameters with valid confidence regions.
In this article we consider the parameter risk in the context of internal modelling of the reserve risk under Solvency II. We discuss two opposed perspectives on parameter uncertainty and point out that standard methods of classical reserving focusing on the estimation error of claims reserves are in general not approp…
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…
StratPPI improves prediction-powered inference with stratified sampling.
In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controll…
FP-UCB algorithm achieves bounded regret for finitely parameterized multi-armed bandits.
Reduces change detection to estimation using confidence sequences.
Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based learning from errors (DBLE). DBLE bases its confidence estimation on distances in the representation space. In DBLE, we first adapt prototypi…
Paper improves confidence intervals for LSA with multiplier bootstrap.