Proposes tau-FPL for efficient false-positive rate control in linear time.
problem Learning classifiers with strict false-positive rate constraints.
method Scoring-thresholding approach with linear time efficiency.
result Superior performance over existing approaches in false-positive rate control.
Two kernel Stein tests control decision errors in non-parametric model comparison.
problem Non-parametric multiple model comparison.
method Two statistical tests controlling false positive and false discovery rates.
result The first test has a higher true positive rate than the second under appropriate conditions.
Paper controls false positives in high-dimensional models using a novel approach.
problem Controlling false positives in high-dimensional models with the Lasso.
method Recast SQRT-Lasso as a false positive control method, extend to all GLMs, use fast Lasso solvers.
result Shows novel false positive control using random weighted self-normalized sums in finite samples.
Reduces false positives in lung nodule detection by using unlabeled data.
problem Lack of labeled data for training supervised algorithms in medical imaging.
method Uses pseudo-negative labels from unlabeled data to refine a pulmonary nodule detection network.
result False positive rate reduced from 0.4864 to 0.1266 while maintaining sensitivity.
The paper reviews techniques for detecting errors in semantic segmentation models.
problem Detecting false positives and false negatives in semantic segmentation models.
method Uncertainty quantification techniques applied to semantic segmentation.
result Techniques for detecting false positives and false negatives are proposed and discussed.
New method reduces false positives in weakly supervised pixel-level localization.
problem Reduces false positives in weakly supervised pixel-level localization.
method Proposes a deep learning method using conditional entropy to constrain the localizer.
result Significant improvements in image-level classification and pixel-level localization.
A new method for multiple testing reduces false discoveries while maximizing power.
problem Maximizing statistical power while controlling false discoveries in multiple testing scenarios.
method Adaptive sampling approach inspired by multi-armed bandits to minimize sample size.
result The method achieves sample complexity close to information theoretic lower bounds and outperforms uniform sampling.
New method calibrates false detection rates in sequential change detection.
problem Challenges in setting time-invariant thresholds for false positives.
method Simulation-based approach to time-varying thresholds.
result Accurately targets desired expected runtime while keeping false positive rate constant.
Study controls error rates of binary classifiers using hypothesis testing.
problem Traditional binary classifiers have uncontrolled error rates.
method Combines binary classification with statistical hypothesis testing.
result Trained classifiers can be made to meet target error rate thresholds.
PatternLocal improves XAI for non-linear models by suppressing suppressor variables.
problem Suppressor variables cause false-positive feature attributions in non-linear models.
method PatternLocal uses locally linear surrogate models and transforms weights into a generative representation.
result PatternLocal reduces false-positive attributions and provides more reliable explanations.
An adjusted NN algorithm reduces false negatives in imbalanced data.
problem Learning from imbalanced data, focusing on reducing false negatives.
method Introduces a reweighted distance scheme to modify Voronoi regions and decision boundaries.
result The method yields the best performance, especially when combined with sampling methods.
A statistical test controls false positives in anomaly localization using diffusion models.
problem Uncertainty and bias in generative models for anomaly localization.
method Selective inference to quantify significance and control false positives.
result The method effectively controls false positive detection rates.
New method improves feature selection by integrating stability paths.
problem Improving feature selection with tighter false positive control.
method Integrating stability paths to strengthen theoretical bounds on E(FP).
result Significantly more true positives with same E(FP) control.
Evolutionary algorithm improves DNN watermarking with fewer false positives.
problem Protecting deep learning models from piracy and proving ownership.
method Evolutionary algorithm for generating and optimizing trigger patterns.
result Reduces false positive rates in DNN watermarking.
AnyThreat detects insider threats with minimal false positives.
problem High false positives in detecting insider threats.
method Opportunistic knowledge discovery system with four components: feature engineering, oversampling, class decomposition, and classification.
result Detects 87.5% of malicious insider threats with minimal false positives.
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.
Study compares shallow and deep learning for MS lesion segmentation.
problem Automated segmentation of white matter lesions in early-stage MS patients.
method Training and testing shallow and deep learning architectures on 32 patients.
result Combining shallow and deep architectures improves lesion-wise metrics.
Algorithm reconstructs triangle-free networks from data, certifying correctness.
problem Reconstructing triangle-free dynamic networks from observational data.
method Developed an algorithm for triangle-free networks, providing guarantees on correctness.
result Algorithm either certifies correctness or outputs a sparser graph with no false positives.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
problem Challenges in setting OOD detection thresholds for safety-critical applications.
method Mathematically grounded framework leveraging expert feedback to dynamically update thresholds.
result Guaranteed to meet FPR constraint while minimizing human feedback, maintaining FPR at most 5%.
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.
The paper introduces a method to incorporate feedback into tree-based anomaly detection to reduce false positives.
problem Difficulty in human analysts examining high-ranking anomalies due to false positives.
method Incorporates simple binary feedback into tree-based anomaly detectors, focusing on the Isolation Forest algorithm.
result Significantly improves the performance of the Isolation Forest algorithm by reducing false positives.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.
Proposes cost-sensitive feature selection for SVMs.
problem Asymmetric misclassification costs in feature selection.
method Mathematical optimization-based approach for SVMs.
result Substantial reduction in feature count with desired error rates.
3D G-CNNs reduce false positives in lung nodule detection.
problem Reducing false positives in pulmonary nodule detection.
method Used 3D roto-translation group convolutions (G-Convs) instead of traditional convolutions.
result 3D G-CNNs achieved FROC scores close to those of a CNN trained on ten times more data.
New neural network outperforms existing methods in scene matching.
problem Automated scene matching with high accuracy and low false positives.
method Convolutional hashing using a new loss function and training scheme.
result Significantly higher true positive rate and 100-fold reduction in false positives.
New algorithm balances user reward and statistical inference by mixing TS with UR based on difference size.
problem Combining statistical inference with user reward in adaptive experiments.
method TS-PostDiff algorithm that uses UR when differences are small and TS when large.
result TS-PostDiff reduces false positives and increases statistical power for small differences, while maximizing reward for large ones.
Benchmarking recursive collapse claims with a new framework under false-positive control.
problem Evaluating recursive systems for failure patterns and warning claims.
method Developed Loopzero framework for testing recursive failures, specified claim boundaries in Lean, evaluated under FP constraint, and compared with standard detectors.
result No standard detectors or Loopzero's pre-registered quantile detector achieved the required operating point under the false-positive contract.
New method improves false-/true-positive-rate estimation in fraud detection with noisy labels.
problem Estimating FPR/TPR in fraud detection with class-conditional label noise.
method Directly cleaning model's validation data to de-correlate cleaning error with model scores.
result Improves accuracy of FPR/TPR estimates, especially in asymmetric label noise scenarios.
This work improved clustering methods by analyzing various datasets and dendrograms.
problem Avoiding false positives in clustering, especially for unimodal and bimodal data.
method Applied agglomerative clustering methods (single, average, median, complete, centroid, Ward's) to various datasets.
result Many methods detected two clusters in unimodal data, with single-linkage being more resilient.
Paper estimates FPR of Bayes classifier using soft labels.
problem Determining optimal classifier performance.
method Uses soft labels and denoising technique.
result Consistent and unbiased FPR estimator developed.
New method corrects biased comparisons in two-group data.
problem Unreliable inferences from biased sampling.
method Developed an inference method resilient to sampling biases.
result Controls false positives under moderate bias levels.
Transformer learns representations from time series data for money laundering detection.
problem Detecting money laundering using structured time series data.
method Contrastive learning for representation learning, followed by scoring and thresholding.
result Transformer outperforms rule-based and LSTM methods in detecting money laundering with controlled false positives.
Stability Selection improves structured variable selection but requires careful tuning.
problem Finding a right-sized model or controlling false positives in structured selection problems.
method Stability Selection applied to group lasso and structured input-output lasso.
result Stability Selection often increases power but reduces error control reliability in structured settings.
AI detects LDDoS attacks by analyzing TCP connection parameters.
problem Detecting low-rate LDDoS attacks that overwhelm server connections.
method AI algorithms trained on simulated and real-world datasets using TCP flow features.
result Decision trees and k-NN achieved high accuracy in classifying attacks, with low false positives and negatives.
Algorithm detects concept drift and adapts models in streaming data.
problem Concept drift in streaming data renders models inaccurate.
method Adaptive learning algorithm that detects drifts and reacts to them.
result Risk competitive to an algorithm with perfect drift knowledge.
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.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
Active learning improves network intrusion detection with minimal labeled data.
problem Finding new attack vectors in low-volume traffic.
method Proposes an active learning framework to minimize labeled data while improving detection quality.
result Active learning with minimal labeled data significantly improves anomaly detection in network traffic.
Deep Belief Network reduces false positives in risky host detection.
problem Overwhelming alerts from SIEM systems, focusing on reducing false positives.
method Combines alert information, logs, and analyst results; uses text mining and graph-based methods for feature generation; compares against various machine learning algorithms.
result Deep Belief Network outperforms other algorithms, improving detection by six times.
MatchGNet detects malware by learning program behavior graphs.
problem Malware evasion through obfuscation and high false positives in traditional detection methods.
method Heterogeneous Graph Matching Network model that learns graph representation and similarity metrics.
result MatchGNet reduces false positives by 50% while maintaining zero false negatives.
The paper tackles fairness in overlapping populations using online learning techniques.
problem Improving fairness to subgroups in settings with overlapping populations and sequential predictions.
method The approach draws from the sleeping experts literature in online learning to achieve a goal of unweighted average of false negative and false positive rate for overlapping populations.
result It shows that satisfying the guarantee for multiple overlapping groups is not straightforward and can be statistically impossible even when predictors perform well separately on each subgroup.
The paper examines how machine learning tools in justice settings can unfairly affect different racial groups.
problem Machine learning tools in justice settings can unfairly affect different racial groups.
method Exploring different ideas of racial equity and their computational trade-offs.
result Computation alone is unlikely to solve the unfairness in machine learning tools for justice settings.
Statistical evaluation of machine learning models can lead to false positives.
problem Statistical significance in model comparison can be misleading.
method Evaluation using train, dev, and test sets with statistical significance testing.
result Statistical significance does not necessarily indicate a superior learning approach.
A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.
problem The asymmetric costs of false positives and false negatives in automated pipelines.
method Introducing Weighted Regret and Decoupled-OMT (DOMT) to unify FDR and power evaluation.
result DOMT achieves an order-optimal sublinear mitigation of threshold depletion in bursty environments.
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity---meaning that the fraction of variables with a n…
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.
Cheap permutation tests speed up distribution testing without sacrificing accuracy.
problem Efficiently testing distribution differences and independence.
method Group datapoints into bins and permute only these bins, using stored sufficient statistics.
result Cheap permutation tests maintain the accuracy and optimality of standard tests but are significantly faster.
Crowdsourced algorithms identify fake news on Twitter.
problem Identifying fake news on social media platforms.
method Evaluation of reputation algorithms on a large dataset of Twitter news.
result Simple crowdsourcing-based algorithms can identify a significant portion of fake news with low false positive rates.