Enhances PLDA scoring for multiple nuisance conditions.
problem Scoring PLDA models for multiple nuisance conditions.
method Developed likelihood ratios for scoring with multiple nuisance conditions.
result Improved PLDA model for joint analysis of multiple conditions.
New method for inference on strongly identified functionals even when nuisance functions are weakly identified.
problem Inference on continuous linear functionals of weakly identified nuisance functions defined by conditional moment restrictions.
method Proposes penalized minimax estimators for both the primary and debiasing nuisance functions, which can converge to fixed limits regardless of nuisance identifiability.
result Proves the asymptotic normality of a debiased estimator for the functional of interest, leading to asymptotically valid confidence intervals.
Optimally estimates a functional using nuisance function tuning and sample splitting.
problem Estimating optimal rates for a doubly robust functional.
method Combines nuisance function tuning and sample splitting strategies.
result Shows optimal rates of convergence for various estimators.
Combines VAE and adversarial censoring for invariant representations.
problem Learning invariant representations from data with nuisance variables.
method Adversarial censoring in conditional VAEs to prevent nuisance variable recovery.
result Achieves invariance while preserving model learning performance.
The paper provides guarantees for statistical learning with a nuisance parameter.
problem Statistical learning with an unknown nuisance parameter.
method Two-stage sample splitting meta-algorithm for target and nuisance parameters.
result Nuisance estimation error impacts excess risk bound of second order under Neyman orthogonality.
NURD improves model performance by distilling representations independent of nuisance variables.
problem Models trained under spurious correlations may fail on data with different nuisance-label relationships.
method Developed Nuisance-Randomized Distillation (NURD) to find representations independent of nuisance variables.
result NURD finds representations that perform better regardless of nuisance-label relationships.
Localized debiased machine learning simplifies estimating quantile treatment effects.
problem Estimating quantile treatment effects in causal inference with many covariates and flexible relationships.
method Localized debiased machine learning (LDML) avoids learning the full nuisance function by estimating only at a single initial guess.
result LDML enables practically-feasible and theoretically-grounded efficient estimation of quantile treatment effects.
New convergence guarantees for learning with unknown nuisance parameters.
problem Learning problems with unknown nuisance parameters.
method Stochastic gradient optimization with Neyman orthogonality and approximately orthogonalized updates.
result Stochastic gradient algorithms can converge under conditions of nuisance parameters.
Orthogonal Random Forest improves causal inference by reducing sensitivity to estimation error.
problem Improving causal inference by reducing sensitivity to estimation error of nuisance parameters.
method Combines Neyman-orthogonality with generalized random forests to estimate conditional moment models.
result Achieves the same error rate as an oracle with a priori knowledge of nuisance parameters under mild assumptions.
Optimal first-order methods are shown to be fundamental limits in functional estimation.
problem Optimal functional estimation under weak conditions.
method Formalization of functional estimation with black-box nuisance function estimates and derivation of minimax lower bounds.
result First-order methods are optimal under weak conditions, but higher-order methods can outperform them when nuisance function structure is known.
Joint PLDA models two factors for better speaker verification.
problem Improving speaker verification accuracy with a nuisance condition.
method Generalizes PLDA to model two sample-dependent factors, modifies training procedure.
result Significant performance gains in multilingual speaker-verification tasks.
Optimal tuning for estimating ECC in proportional asymptotics.
problem Estimating Expected Conditional Covariance (ECC) under proportional asymptotics.
method Debiased ridge regression estimators for nuisance functions, sample splitting strategies, and asymptotic variance analysis.
result Prediction-optimal tuning parameters may not minimize asymptotic variance of ECC estimator.
A new framework evaluates HTE estimators using relative error.
problem Lack of robust evaluation methods for HTE estimators.
method Proposes a relative error-based evaluation framework and neural network architecture to estimate nuisance parameters and robustly compare HTE estimators.
result Demonstrates reliable comparisons and improved HTE estimation through the proposed framework and learning algorithm.
Bayesian method corrects bias in treatment effect estimation.
problem Estimating treatment effects from observational data with high-dimensional nuisance parameters.
method Bayesian debiasing, targeted modeling, sample splitting.
result Marginal posterior for ATE satisfies Bernstein-von Mises theorem under correct nuisance model specification.
Accuracy on in-distribution data correlates with out-of-distribution data when data is noisy or contains nuisance features.
problem Correlation between in-distribution and out-of-distribution accuracy in noisy or feature-rich data.
method Analyzes the impact of noise and nuisance features on model performance.
result Accuracy on in-distribution and out-of-distribution data can become negatively correlated in noisy or feature-rich data.
Paper introduces DRM for selecting robust CATE estimators.
problem Selecting CATE estimators without counterfactual outcomes.
method Distributionally Robust Metric (DRM) for CATE estimator selection.
result DRM selects robust CATE estimators robust to distribution shift.
Optimal CATE estimation with structured contrast functions using KRR.
problem Estimating CATEs with complex response functions in RKHS.
method Unified two-stage kernel ridge regression method for structured contrast functions.
result Minimax rates governed by contrast function complexity, enabling adaptation.
This guide simplifies high-probability regret bounds in empirical risk minimization.
problem High-probability regret bounds in empirical risk minimization.
method Modular presentation, three-step recipe, localized Rademacher complexity, local maximal inequalities, metric-entropy integrals.
result Recover familiar rates for various function classes and derive regret bounds for nuisance components.
A TCL framework improves causal effect estimation in limited data.
problem Improving causal effect estimation accuracy in limited data.
method Transfer Learning (TCL) with ℓ1 regularization for nuisance models.
result Non-asymptotic recovery guarantees for exttt{ ℓ 1 \ell_1 ℓ 1 -TCL} in high-dimensional settings. New estimator optimizes black-box model errors in semiparametric estimation.
problem How nuisance estimation errors affect low-dimensional target parameters in semiparametric models.
method Proposed a new estimator achieving a sharper rate of convergence.
result The first-order stochastic error of nuisance estimation can be eliminated.
Framework learns to separate predictive from nuisance factors for robust machine learning.
problem Supervised models associate irrelevant factors with prediction targets, hurting generalization.
method Information-theoretic formulation for discovering and separating predictive and nuisance factors.
result State-of-the-art performance achieved without requiring nuisance annotations.
Extends robust methods for causal inference, improving estimator performance.
problem Estimating causal effects in the presence of latent confounders.
method Minimax kernel machine learning for doubly robust functionals.
result Proposed method leads to robust and high-performance estimators.
Proposes efficient estimators for weighted cumulative treatment effects in observational studies.
problem Inconsistent and inefficient estimators due to model misspecification and lack of overlap.
method Double/debiased machine learning for weighted cumulative causal effects.
result Proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds.
Paper introduces metrics to assess and control nuisance factors in sentiment analysis.
problem Challenges in learning invariant representations for sentiment analysis due to entangled nuisance factors.
method Developed two generalization metrics and a data filtering approach to control nuisance factors.
result Simple text classification baseline can be badly affected by product ID in sentiment analysis.
Approach for selecting features by discarding nuisance and correlated ones.
problem Large datasets with correlated and nuisance features.
method Laplacian score criterion, autoencoder architecture, concrete layer.
result Outperforms similar approaches in clustering performance.
Machine learning in high-energy physics faces challenges from nuisance parameters, which are reviewed and techniques to mitigate their impact are discussed.
problem Impact of nuisance parameters on machine learning performance in high-energy physics.
method Review and discussion of techniques including nuisance-parameterized models, modified or adversary losses, semi-supervised learning, and inference-aware techniques.
result Various methods to reduce the impact of nuisance parameters and improve model performance in high-energy physics.
Unified framework for invariance to nuisance and bias factors in neural networks.
problem Inducing independence to nuisance and bias factors in neural networks without labeled data.
method Unified invariance framework using competitive training between prediction and reconstruction tasks, coupled with disentanglement and adversarial learning.
result Outperforms previous works at inducing invariance to nuisance factors and achieves state-of-the-art performance at learning independence to biasing factors.
Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.
problem Bayesian active learning with nuisance parameters leads to bias and dilemmas.
method Characterizes and mitigates negative interference by accurately estimating nuisance parameters.
result The extent of negative interference can be extremely large, and accurate estimation of nuisance parameters is critical.
DML addresses biases in machine learning by estimating nuisance functions.
problem Bias in machine learning models due to nuisance functions.
method Double/Debiased Machine Learning (DML) approach to reduce biases.
result DML allows flexible estimation of nuisance functions without auxiliary assumptions.
New method uses MMD estimators to enforce model invariance with missing data.
problem Models trained on missing data can fail on related test distributions.
method Derives MMD estimators for enforcing model invariance under missing nuisances.
result Optimizing through MMD estimates achieves similar test performance to using full data.
Improved estimators for causal inference using cross-fitting and undersmoothing.
problem Estimating expected conditional covariance in causal inference.
method Double cross-fit doubly robust (DCDR) estimators with undersmoothing for non-smooth nuisance functions.
result DCDR estimators achieve n \sqrt{n} n -consistency and asymptotic normality under minimal conditions. AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
New methods calibrate causal estimates using standard predictive models.
problem Calibrating causal treatment effect estimates.
method Developed algorithms to transform causal estimation into standard calibration.
result General algorithms for causal calibration using standard predictive models.
The paper uses neural networks to estimate treatment effects even with many confounders.
problem Estimating treatment effects with a growing number of confounders.
method General optimization framework using neural networks to approximate nuisance functions.
result Neural networks can handle a diverging number of confounders and alleviate the curse of dimensionality.
New method improves robustness of double robust estimators under complete misspecification.
problem Improper performance of double robust estimators when all nuisance functions are misspecified.
method DR+ACC, an adaptive correction clipping method.
result DR+ACC ensures bounded error and maintains semiparametric efficiency.
Method eliminates nuisance functions for spatially varying causal effects.
problem Inferring causal effects in presence of spatial confounding.
method Eliminates nuisance functions, mitigates errors-in-variables.
result Robust and accurate inference of spatially varying heterogeneous causal effects.
A new method debiases multiple target parameters without IFs.
problem Debiasing multiple target parameters in nonparametric models.
method Kernel Debiased Plug-in Estimation (KDPE) using TMLE and reproducing kernel Hilbert spaces.
result KDPE simultaneously debiases all pathwise differentiable target parameters.
Paper presents a new doubly robust estimator for survival analysis with improved consistency.
problem Consistency of doubly robust estimators in high dimensions with flexible data-adaptive methods.
method Data-adaptive regression estimators, Gaussianization, cross-fitting.
result The estimator converges at n 1 / 2 n^{1/2} n 1/2 rate for a large class of data-adaptive nuisance estimators. New method for estimating global and local parameters using regularized Riesz representers.
problem Estimating global and local parameters in complex models robustly.
method Adaptive inference methods based on ℓ1 regularization, including Riesz representer as a nuisance parameter.
result Non-asymptotic and asymptotic uniform validity for honest confidence bands.
Proposes debiasing strategy for ill-posed regression problems.
problem Estimating functions with conditional moment restrictions, especially when estimators are sensitive to misspecification.
method Debiased estimation using influence function of modified mean squared error.
result Demonstrates finite-sample convergence rate and robustness to misspecification.
Framework for multi-task learning with semiparametric models and nuisance parameters.
problem Improving parameter estimation from diverse, heterogeneous datasets.
method Late fusion multi-task learning framework with two-step process: individual task learning followed by adaptive aggregation.
result The method achieves faster convergence rates compared to individual task learning when tasks share similar parametric components.
A new machine learning method handles nuisance parameters for better unfolding in particle physics.
problem Improving statistical correction of cross sections in complex particle physics detectors.
method Profile OmniFold, a machine learning-based Expectation-Maximization procedure that incorporates nuisance parameters.
result Demonstrated the effectiveness of Profile OmniFold on both simulated and real data.
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
problem Computational cost and limited applicability of current SBI methods for realistic analyses.
method Simulation-Based Inference with Factorizable Normalizing Flows to model systematic variations.
result Efficient profiling of nuisance parameters and multivariate DoI in complex analyses.
New methods correct for time dependencies in IV regression for time series data.
problem Inferring causal effects from time series data with unobserved confounders.
method Proposes new methods for consistent estimation of causal effects in time series models using nuisance covariates and graph marginalization.
result Identifies and corrects for dependencies in the past, leading to consistent estimation of causal effects.
Develops methods to estimate ratios of conditional expectation functions.
problem Estimating ratios of conditional expectation functions in causal inference.
method Orthogonal series estimator combined with debiased machine learning techniques.
result Valid pointwise and uniform asymptotic results for estimation and inference on CEFR.
New method corrects biased predictions and uncertainty estimates in classification with nuisance parameters.
problem Tackles biased predictions and invalid uncertainty estimates in classification with nuisance parameters.
method Proposes a method that estimates ROC across the entire nuisance parameter space to devise invariant cutoffs.
result Demonstrates effective domain adaptation and valid prediction sets with high power.
A new method for CI construction from nuisance parameter estimators.
problem Building confidence intervals from nuisance parameter estimators.
method Collaborative TMLE (C-TMLE) for inference.
result The C-TMLE yields a CI under certain conditions.
The paper proposes a method to estimate complex models using machine learning.
problem Estimating the impact of welfare reform on women's welfare participation.
method Regularized orthogonal machine learning for non-linear semiparametric models.
result The proposed Lasso estimator converges at the oracle rate, preserving the single index property.