New method controls false discoveries in financial asset pricing.
problem Controlling false discoveries in time series with unknown correlations.
method Double bootstrapping method to control false discovery rate.
result Superior statistical power and controlled false discovery rate.
New methods control false discoveries near the boundary in conformal novelty detection.
problem Over-optimistic assessments near the rejection threshold in conformal novelty detection.
method Support line (SL) correction and alternative procedures to control boundary false discovery rate (bFDR).
result New procedures control the boundary false discovery rate (bFDR) in the conformal setting.
Private online FDR control for adaptive testing under differential privacy.
problem Controlling false discoveries in adaptive multiple hypothesis testing with privacy constraints.
method Private online algorithms based on non-private results, ensuring privacy and statistical performance.
result Strong guarantees for privacy and statistical performance in FDR and power.
Nonparametric IPSS selects features with false discovery control.
problem Feature selection in high-dimensional data with theoretical false discovery control.
method Integrated Path Stability Selection (IPSS) applied to nonparametric feature importance scores.
result IPSS accurately controls false discovery rate and detects more true positives than existing methods.
Finite resources limit false discovery rate control in structured hypothesis spaces.
problem Controlling false discovery rate in hypothesis testing with finite data and structured hypothesis spaces.
method Framework for exact FDR control and adaptive power maximization.
result Exact FDR control and adaptive power maximization.
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.
New method controls false edge detections in Gaussian graphical models.
problem High false edge detections in well-established estimators.
method Nodewise variable selection approach to control false discovery rate.
result Significant gain in performance compared to competing methods.
New method controls false discoveries in online testing with deadlines.
problem Controlling false discoveries in online hypothesis testing with decision deadlines.
method Benjamini-Hochberg-type procedure over a moving window of hypotheses with adaptive threshold parameters.
result Controls false discovery rate at every stage and adaptively chosen stopping times.
Enhances FDR control in variable selection using neural networks.
problem Balancing rigorous error control with statistical power in high-dimensional variable selection.
method Learning-augmented T-Rex Selector framework with a neural network trained on synthetic datasets.
result Achieves superior detection of true variables compared to existing approaches.
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.
Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.
problem Vulnerability of deep learning models to membership inference attacks (MIAs).
method Designs a novel membership inference attack method that provides FDR guarantees.
result Demonstrates the effectiveness of the method in various settings.
The paper controls false discovery rate in link prediction using conformal inference.
problem Identifying true edges in a graph while controlling false discoveries.
method Proposes a novel method based on conformal inference to control false discovery rate (FDR) in link prediction.
result Empirically demonstrates FDR control for both simulated and real data.
New rules control false discoveries in online anomaly detection for time series data.
problem Controlling false discoveries in anomaly detection for time series data.
method Novel online false discovery rate control (FDRC) rules for time series anomaly detection.
result Ensures high power in detecting anomalies even when the alternative is rare and test statistics are serially dependent.
Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses H(n)=(H1,…,Hn), Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…
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.
Paper proposes a privacy-preserving method to control false discoveries.
problem Protecting individual information in hypothesis tests while controlling false discoveries.
method Differentially private adaptive FDR control method with privacy guarantee.
result The method controls the FDR metric exactly at a user-specified level with privacy.
New method controls false discoveries in structured hypothesis spaces.
problem Controlling false discoveries in large-scale, interconnected hypothesis spaces.
method Reproducing Kernel Hilbert Space (RKHS) optimization for structured FDR control.
result Unified framework for continuous domains, graphs, and hierarchies.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
CatNet controls FDR in LSTM models using SHAP feature importance and Gaussian mirrors.
problem Controlling False Discovery Rate (FDR) in LSTM models with feature selection.
method CatNet uses SHAP values for feature importance and Gaussian Mirror algorithm for FDR control. It introduces a kernel-based independence measure to handle feature correlations.
result CatNet reduces overfitting and improves model interpretability on simulated and real-world data.
DeepFDR uses deep learning for better FDR control in neuroimaging data.
problem Spatial dependence among voxel-based tests in neuroimaging data.
method DeepFDR leverages unsupervised deep learning-based image segmentation.
result DeepFDR outperforms existing methods in FDR control and computational efficiency.
e-LOND algorithm controls FDR in online testing with arbitrary dependencies.
problem Online testing of hypotheses with unknown dependencies.
method e-LOND algorithm for FDR control under arbitrary dependence.
result e-LOND provides more power than existing methods through simulations.
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.
fcHMRF-LIS controls FDR in neuroimaging data, improving power and scalability.
problem Complex spatial dependencies and high variability in FDR control methods for neuroimaging data.
method fcHMRF-LIS integrates LIS-based testing with fcHMRF to model spatial structures efficiently.
result fcHMRF-LIS achieves accurate FDR control, lower FNR, and higher true positives compared to existing methods.
New findings control FDR for online testing methods under positive dependence.
problem Maintaining FDR control for online testing methods under positive dependence.
method Developed new methods to control FDR for online testing procedures under positive dependence.
result SAFFRON and LORD control FDR under positive dependence, not just conditional superuniformity.
New algorithm for adaptive experimental design in scientific settings.
problem Identifying true positives while controlling false discoveries in adaptive experimental design.
method Provably sample efficient adaptive algorithm for FDR control.
result First provably sample efficient adaptive algorithm for adaptive experimental design.
Estimates target GGM using auxiliary studies with false discovery rate control.
problem Estimating high-dimensional GGMs from related studies.
method Transfer learning with Trans-CLIME and debiased Trans-CLIME estimators.
result Debiased Trans-CLIME estimator provides element-wise asymptotic normality and false discovery rate control.
We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider n distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (actual positives). In addi…
New method controls false discoveries in real-time data streams.
problem Online testing of hypotheses with strict error constraints and no future data.
method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.
result Substantial power gain over existing online testing rules.
Paper proposes AdaDetect for FDR-controlled novelty detection.
problem Semi-supervised novelty detection with probabilistic classification.
method Data-adaptive learning of transformation to control FDR.
result Control of false discovery rate on detected novelties.
The paper optimizes A/B tests by balancing lift and cost in large-scale settings.
problem Balancing lift and cost in A/B tests for large-scale experimentation.
method Empirical Bayes approach using a greedy knapsack algorithm to rank experiments based on lift-to-cost ratio, incorporating local false discovery rate (lfdr).
result The proposed method maximizes expected profit while controlling false discovery rate, demonstrating superior performance in large-scale settings.
The paper tackles noisy matrix completion by developing new statistics and controlling false discovery rate.
problem Testing multiple linear forms for noisy matrix completion with low-rank structure.
method Introducing new statistics with sharp asymptotics for individual tests, controlling FDR via data splitting and aggregation.
result Valid FDR control can be achieved with guaranteed power under nearly optimal sample size requirements.
With the rapid growth of crowdsourcing platforms it has become easy and relatively inexpensive to collect a dataset labeled by multiple annotators in a short time. However due to the lack of control over the quality of the annotators, some abnormal annotators may be affected by position bias which can potentially degra…
Unified framework controls false discovery rate in bandit multiple testing.
problem Designing adaptive algorithms to identify true discoveries in multiple hypothesis testing.
method Unified modular framework using e-processes for FDR control in arbitrary settings.
result Unified framework ensures FDR control for dependent and simultaneous arm queries.
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.
Sparse PCA selects variables with FDR control for improved performance.
problem Sparse PCA selects irrelevant variables when maximizing explained variance.
method Proposes FDR-controlled selection using T-Rex selector.
result Significant performance improvement over traditional sparse PCA.
Bayesian approach controls FDR in high-dimensional models.
problem High-dimensional variable selection and inference.
method Adapted Mirror Statistic to Bayesian framework for FDR control.
result Effective FDR control without data splitting.
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…
T-Rex selector selects variables fast and controls FDR in high-dimensional data.
problem Variable selection in high-dimensional data with FDR control.
method Fused solutions of early terminated random experiments.
result FDR control at target level with high variable selection power.
In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value is observed. Alpha-investing algorithms to control the false discovery rate (FDR…
NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.
problem Controlling FDR in complex large-scale problems with indirect relations among covariates.
method NeurT-FDR uses a deep Black-Box framework that parametrizes test-level covariates as a neural network and adjusts auxiliary covariates through a regression framework.
result NeurT-FDR makes substantially more discoveries in real datasets compared to competitive baselines.
AdaPT-GMM improves multiple testing power with covariates.
problem Powerful and robust multiple testing with covariates.
method Covariate-assisted Gaussian mixture model with adaptive thresholding.
result AdaPT-GMM delivers high power in various scenarios.
Biological research often involves testing a growing number of null hypotheses as new data is accumulated over time. We study the problem of online control of the familywise error rate (FWER), that is testing an apriori unbounded sequence of hypotheses (p-values) one by one over time without knowing the future, such th…
New algorithms control FDX while achieving more power in online multiple testing.
problem Problems with previous online multiple testing methods, including high FDX and low power.
method Developed new dynamic algorithms that adjust testing levels based on accumulated wealth.
result SupLORD algorithm achieves higher power and FDR control in synthetic experiments.
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.
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backw…
AKO improves stability and power of Knockoff inference.
problem Instability of Knockoff-based inference.
method Aggregation of Multiple Knockoffs (AKO) method.
result AKO maintains FDR control while improving stability and power.
The paper provides high-probability bounds on false discovery proportions in conformal inference.
problem Existing methods fail to provide high-probability bounds on the realized false discovery proportion.
method Constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution.
result Establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds.