Unified framework for FDR control in knockoffs, validating Gaussian knockoffs.
problem Asymptotic FDR control in knockoffs with user-specified distributions.
method Unified theoretical framework, three conditions on approximate knockoff statistics, Gaussian knockoffs generator based on moments matching.
result Gaussian knockoffs generator achieves asymptotic FDR control.
ARK improves knockoffs robustness to feature distribution misspecification.
problem Robustness of knockoffs inference to misspecified feature distributions.
method Coupling approximate knockoffs with model-X knockoffs to achieve FDR and FWER control.
result The approximate knockoffs procedure can control FDR and FWER asymptotically.
The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoff is that if we have a good model of the features X, then we can identify salient features without knowing anything about how the outcome Y depends on X. An important dra…
Paper proposes a privacy-preserving knockoff inference method.
problem Ensuring privacy in model-X knockoff inference.
method Differential privacy framework for knockoff inference.
result Guaranteed FDR control with privacy protection.
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses the instability inherent to the random nature of Knockoff-based inference. Specifically, AKO improves both the stability and power compared with…
DDLK uses deep learning to find important features in models.
problem Discovering important features in black box models like deep neural networks.
method DDLK directly minimizes KL divergence to generate knockoffs that obey the swap property.
result DDLK outperforms baselines in discovering important features while controlling false discovery rate.
Efficient knockoffs for large-scale feature selection.
problem Large-scale feature selection problems.
method Gaussian model-X knockoffs with efficient methods for solving semidefinite programs.
result Efficient knockoffs can be generated with linear complexity in the dimension.
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion i…
Knockoffs method selects financial factors, controlling false discoveries.
problem Controlling false discoveries in financial factor selection.
method Apply knockoff procedure to build fake factors.
result Shows versatility in fund replication and network inference.
An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For exam…
Knoop enhances variable selection with over-parameterization and knockoffs.
problem Challenges of variable selection in high-dimensional datasets.
method Generates knockoff variables, integrates them into an over-parameterized model, and uses anomaly-based significance tests.
result Superior performance in variable selection compared to existing methods.
Private variable selection method controls FDR with simulations showing reasonable power.
problem Performing variable selection with privacy constraints.
method Private knockoff filter using Gaussian and Laplace mechanisms.
result Achieves controlled false discovery rate (FDR) in variable selection.
Extends knockoff filter for composite null hypotheses in variable selection.
problem Handling composite null hypotheses in variable selection.
method Developed two methods for composite inference with knockoffs: S-OLS and FRPP.
result Proposed heuristic variants of S-OLS outperforming BH procedure for composite nulls.
Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the power and robustness for the model-free knockoffs procedure introduced recently in Candès, Fan, Jans…
DiffKnock improves feature selection in neural networks with complex dependencies and non-linear associations.
problem Selecting important features in neural networks with complex dependencies and non-linear associations.
method DiffKnock uses diffusion models to generate knockoffs and neural network statistics to measure feature importance.
result DiffKnock outperforms existing methods in detecting non-linear associations and preserving feature dependencies.
DeepLINK-T uses deep learning and knockoffs for time series data.
problem Interpreting and reproducible deep learning models for high-dimensional time series data.
method Combines deep learning with knockoffs for FDR control in feature selection for time series models.
result DeepLINK-T effectively controls FDR while demonstrating superior feature selection for high-dimensional longitudinal time series data.
A new knockoff statistic using conditional prediction function improves variable selection in complex models.
problem Controlling false discovery rate in complex models with nonlinear relationships.
method Introducing a knockoff statistic based on the conditional prediction function for use with machine learning models.
result The CPF statistics provide superior power in detecting prognostic variables over existing knockoff statistics.
Paper proposes knockoff-based methods to simplify deep neural networks by controlling false discovery rates.
problem High-dimensional deep neural networks with many irrelevant parameters and inputs.
method Knockoff methods combined with regularized neural networks for variable screening.
result Proposed algorithms show satisfactory performance in controlling false discovery rates.
A method to remove mean-shift noise from PCA using knockoffs.
problem High sensitivity of PCA to mean-shift contamination in high-dimensional data.
method Introducing knockoff mean-shift perturbation to separate and remove mean-shift components from PCA.
result The mean-shift spikes are spectrally separable from stable eigenvalues, allowing for robust PCA.
Extends model-x framework to handle missing data.
problem Inability to control false selections in missing data settings.
method Posterior sampled imputation, univariate imputation, joint imputation and sampling knockoffs.
result Preserves theoretical guarantees of model-x framework in missing data setting.
Develops robust knockoffs for controlling false discoveries in financial data.
problem Challenges in variable selection with highly correlated data in finance and economics.
method Robustified knockoff framework addressing high dependence and time correlation.
result Identifies new important groups of factors on top of known drivers.
Novel privatization framework for high-dimensional variable selection with differential privacy.
problem High-dimensional controlled variable selection with rigorous FDR control under differential privacy constraints.
method Gaussian Johnson-Lindenstrauss Transformation for privatizing the knockoff matrix.
result The proposed private variable selection procedure maintains statistical power even under strict privacy budgets.
The knockoff filter introduced by Barber and Candès 2016 is an elegant framework for controlling the false discovery rate in variable selection. While empirical results indicate that this methodology is not too conservative, there is no conclusive theoretical result on its power. When the predictors are i.i.d. Gaussian…
FlowSelect uses normalizing flows to control FDR in feature selection.
problem Controlled feature selection with knockoffs often fails to control false discovery rate (FDR).
method FlowSelect uses normalizing flows for accurate feature modeling and a novel MCMC-based p-value calculation to enforce knockoff properties.
result FlowSelect consistently controls FDR and demonstrates greater power compared to competing methods.
A single pre-trained agent guides feature selection using knockoffs.
problem Feature selection challenges in AI-readiness of data.
method Generates knockoff features and uses reinforcement learning.
result Optimal feature subset identified with reduced dependency on target variable.
New methods reduce extrapolation errors in feature importance.
problem Flawed feature importance methods using unrestricted permutations lead to extrapolation errors.
method Three new approaches: conditional model reliance, Knockoffs with Gaussian transformation, and restricted ALE plot designs.
result Theoretical and numerical results show our strategies reduce/eliminate extrapolation.
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features w…
A new method enhances signal recovery with FDR control.
problem Challenging signal recovery in compressive sensing.
method Knockoff-guided compressive sensing framework with FDR control.
result Guaranteed FDR control leads to more accurate signal reconstruction.
Logistic regression with wavelets achieves bacterial infection detection accuracy.
problem Interpreting complex biomedical signal models for high-stakes decisions.
method Wavelet features and knockoff variables for feature selection.
result Logistic regression outperforms neural networks in bacterial infection detection.
A new efficient test addresses limitations of knockoffs for conditional independence testing.
problem Testing conditional independence under model-X assumptions.
method Leave-One-Covariate-Out Conditional Randomization Test (LOCO-CRT)
result LOCO-CRT produces valid p-values for familywise error rate control with minimal variability. The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scientific studies in various disciplines. In this work, we introduce a new method for controlling the FDR in meta-analysis of many decentralized…
Study examines challenges in variable importance ranking due to feature correlation.
problem Challenges in variable importance ranking under correlation.
method Simulation study and theoretical analysis of feature knockoffs and conditional predictive impact (CPI).
result Highly correlated features increase the correlation of knockoff variables, posing a limitation for CPI.
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of ML models has emerged as an important consideration. Confidentiality of ML models can be protected by exposing them to clients only via predi…
New method for mixed data FI controls type I error and achieves high power.
problem Statistical inadequacy of feature importance measures for mixed data.
method Combining CPI framework with sequential knockoffs for mixed data.
result Our method controls type I error and achieves high power for mixed data.
SEEK algorithm selects minimal state in reinforcement learning for better policy learning.
problem Challenges in obtaining a state representation that is parsimonious and satisfies the Markov property.
method SEEK algorithm estimates the minimal sufficient state in reinforcement learning.
result The SEEK algorithm achieves selection consistency in large samples.
GRIP2 improves deep learning feature selection robustness in correlated and noisy data.
problem Identifying predictive features in correlated and noisy data.
method Integrates first-layer feature activity over a two-dimensional regularization surface to control sparsity and geometry, using efficient block-stochastic sampling.
result Demonstrates improved robustness and power in high correlation and low signal-to-noise ratio regimes.
Feature selection is central to contemporary high-dimensional data analysis. Grouping structure among features arises naturally in various scientific problems. Many methods have been proposed to incorporate the grouping structure information into feature selection. However, these methods are normally restricted to a li…
The paper analyzes the power of MX CI tests and finds likelihood-based statistics most powerful.
problem Testing conditional independence under model-X assumptions.
method Conditional randomization test (CRT) and MX knockoffs.
result Likelihood-based statistics are most powerful in MX CI tests.
This paper proposes a model-free and data-adaptive feature screening method for ultra-high dimensional datasets. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model an…
Deep learning has become increasingly popular in both supervised and unsupervised machine learning thanks to its outstanding empirical performance. However, because of their intrinsic complexity, most deep learning methods are largely treated as black box tools with little interpretability. Even though recent attempts …
Paper tackles action selection in deep RL, proposing a data-driven approach.
problem High-dimensional action selection in deep RL environments.
method Data-driven approach with knockoff sampling for minimal sufficient actions.
result Method surpasses alternative techniques in performance and rewards.
Proposes a new feature selection method integrating feature relationships.
problem Feature selection in machine learning models.
method Integrates feature-feature and feature-target relationships via penalized mRMR.
result Correctly identifies inactive features, reducing false discoveries.
We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter. We show on both simulated data and the Norwegian wind farm dataset that the proposed feature importance statistic yields statistically signific…
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set. Building on the knockoff framework of Candès et al. (2018), we develop a novel testing procedure that works in conjunction…
The goal of feature selection is to identify important features that are relevant to explain an outcome variable. Most of the work in this domain has focused on identifying globally relevant features, which are features that are related to the outcome using evidence across the entire dataset. We study a more fine-grain…
Spofe bridges statistical rigor and interpretability in feature extraction from tabular data.
problem Ensuring statistical rigor and interpretability in feature extraction from complex tabular data.
method Spofe combines kernel principal components and sparse polynomial functions with a multi-objective knockoff selection procedure.
result Spofe consistently outperforms other methods in feature selection for regression and classification tasks.
Diamond method controls FDR for trustworthy feature interaction discovery in ML models.
problem Limited interpretability of ML models due to black box nature.
method Diamond method integrates model-X knockoffs framework to control FDR for non-additive interactions.
result Diamond method ensures accurate discovery of feature interactions with FDR control.
Improved GoF statistics using entropy-regularized optimal transport for multivariate rank.
problem Developing efficient multivariate rank statistics for statistical testing and generative modeling.
method Entropy-regularized optimal transport maps to address computational and sample complexity issues.
result Proposed soft rank energy and maximum mean discrepancy achieve fast convergence rates and are differentiable.