arXiv research

The paper discusses selecting predictive models for causal inference, highlighting the challenges and proposing a solution.

The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.

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

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

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…

Approach for selecting features by discarding nuisance and correlated ones.

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

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

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

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…

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…

DML addresses biases in machine learning by estimating nuisance functions.

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

New convergence guarantees for learning with unknown nuisance parameters.

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-awa…

AutoBayes automates Bayesian graph exploration for robust machine learning.

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…

New method improves robustness of double robust estimators under complete misspecification.

This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on. Specifically, an Iteratively Re-Weighted method (IRW) with so…

Bayesian method corrects bias in treatment effect estimation.

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

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

Optimal tuning for estimating ECC in proportional asymptotics.

Accuracy on in-distribution data correlates with out-of-distribution data when data is noisy or contains nuisance features.

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions. Invar…

Localized debiased machine learning simplifies estimating quantile treatment effects.

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…

Optimal first-order methods are shown to be fundamental limits in functional estimation.

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

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

New algorithm profiles nuisance parameters for multidimensional deconvolution.

Corrects mismatch in consistency of nuisance estimators for doubly robust methods.

We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations. In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to pre…

Generative Adversarial Network model for class-imbalanced tabular data.

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…

Extends robust methods for causal inference, improving estimator performance.

A TCL framework improves causal effect estimation in limited data.

A new framework evaluates HTE estimators using relative error.

ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation

This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a n…

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

Proposes efficient estimators for weighted cumulative treatment effects in observational studies.

M2m method improves deep learning performance on class-imbalanced datasets.

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting. We present a novel unsupervised invariance induction f…

New method makes CP intervals locally adaptive using trainable transformations.

Paper introduces DRM for selecting robust CATE estimators.

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…

DisCor corrects reinforcement learning issues by re-weighting collected data.