arXiv research

Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.

Machine learning in high-energy physics faces challenges from nuisance parameters, which are reviewed and techniques to mitigate their impact are discussed.

We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on an unknown nuisance parameter that must be estimated from data. We analyze a two-stage sample splitting meta-algorithm that takes as input ar…

Framework for multi-task learning with semiparametric models and nuisance parameters.

New convergence guarantees for learning with unknown nuisance parameters.

A new machine learning method handles nuisance parameters for better unfolding in particle physics.

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-condit…

We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…

New method corrects biased predictions and uncertainty estimates in classification with nuisance parameters.

DML addresses biases in machine learning by estimating nuisance functions.

New algorithm profiles nuisance parameters for multidimensional deconvolution.

Optimal tuning for estimating ECC in proportional asymptotics.

A new framework evaluates HTE estimators using relative error.

Proposes a robust Q-learning method to improve treatment strategy estimation.

Optimally estimates a functional using nuisance function tuning and sample splitting.

Extends robust methods for causal inference, improving estimator performance.

Double machine learning provides $\sqrt{n}$-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-1/4}$ rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance param…

Bayesian method corrects bias in treatment effect estimation.

A new method debiases multiple target parameters without IFs.

We propose the orthogonal random forest, an algorithm that combines Neyman-orthogonality to reduce sensitivity with respect to estimation error of nuisance parameters with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using rand…

New method for unbinned, profiled unfolding in particle physics.

Paper introduces DRM for selecting robust CATE estimators.

Sharp bounds on ATE with unmeasured confounders, valid even when misspecified.

Adapts MBDOE for real-time parameter estimation in complex systems.

NURD improves model performance by distilling representations independent of nuisance variables.

The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, $n^{1/2}$-consistency of doubly robust estimators is…

Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generativ…

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular f…

A TCL framework improves causal effect estimation in limited data.

A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.

DoubleML is a Python library for causal inference using machine learning.

While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a fini…

A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.

New estimator optimizes black-box model errors in semiparametric estimation.

Proposes debiasing strategy for ill-posed regression problems.

Theory establishes optimal rates for estimating linear functionals without structural assumptions.

The joint PLDA model, is a generalization of PLDA where the nuisance variable is no longer considered independent across samples, but potentially shared (tied) across samples that correspond to the same nuisance condition. The original work considered a single nuisance condition, deriving the EM and scoring formulas fo…

New method for clustering tasks with heterogeneous data.

Approach for selecting features by discarding nuisance and correlated ones.

We address challenges in estimating parameters from adaptively collected data.

This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we es…

New methods calibrate causal estimates using standard predictive models.

New method for inference on strongly identified functionals even when nuisance functions are weakly identified.

Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust neural networks that induces invariance to nuisances through learning to discover and separate predictive and nuisance factors of data. We pre…

New method uses MMD estimators to enforce model invariance with missing data.

AutoBayes automates Bayesian graph exploration for robust machine learning.

This work optimizes statistical inference with neural networks for high-energy physics data.

Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance factor is entangled in a raw text. To our knowledge, a major issue is also…