Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adh…
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48 results for “false-positive control”
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.
In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals. While several approaches have been developed to control the inclu…
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.
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.
In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated …
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We propose two statistical tests, each controlling a different notion of decision errors. The first test, building on the post selection inference…
Representation learning with a transformer by contrastive learning for money laundering detectioncs.LG
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.
The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entai…
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.
Cheap Permutation Testingmath.ST
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.
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%.
A drift detection method for large datasets without labels.
problem Early detection of concept drift in large, unlabeled datasets.
method Classical statistical process control in a label-less setting.
result Better statistical power than previous methods under computational constraints.
A neural network for online NP classification with reduced complexity.
problem Online nonlinear Neyman-Pearson classification.
method Single hidden layer feedforward neural network (SLFN) initialized with random Fourier features (RFFs). Uses stochastic gradient descent for sequential learning.
result Expedited online adaptation and powerful nonlinear Neyman-Pearson modeling.
Paper introduces PTL-SI for statistical inference in TL-HDR, controlling FPR.
problem Quantifying statistical significance in TL-HDR with limited data.
method PTL-SI framework for valid -values in TL-HDR feature selection.
result Valid -values and controlled FPR in TL-HDR feature selection.
The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.
problem Ensuring reliability of feature selection after domain adaptation in high-dimensional regression.
method Proposes a novel method to test features selected by SeqFS-DA with controlled FPR.
result The proposed method controls FPR below a significance level (e.g., 0.05) and enhances statistical power.
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance Systemcs.CV
Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quaranti…
In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to …
SFS-DA method statistically tests FS reliability under domain adaptation.
problem Feature selection reliability under domain adaptation with limited target data.
method Selective Inference framework to control false positive rate and enhance true positive rate.
result SFS-DA method controls FPR below a pre-specified level (e.g., 0.05) while maximizing true positive rate.
New method quantifies deep kNN anomaly detection significance.
problem Lack of uncertainty quantification in deep kNN AD.
method Selective Inference for anomaly scoring.
result Validates AD reliability with controlled false positives.
We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covar…
Paper proposes a statistical test for feature selection pipelines using selective inference.
problem Assessing the significance of feature selection pipelines in data analysis.
method Selective inference technique applied to feature selection pipelines composed of various algorithms.
result The proposed statistical test controls false positive feature selection probabilities.
Study proposes a statistical test for Vision Transformer's attention mechanisms.
problem ViT's attention mechanisms may focus on irrelevant regions, leading to unreliable evidence.
method Selective inference framework to quantify statistical significance of attentions as p-values.
result Proposed method enables reliable quantification of false positive detection probability of attentions.
Simple methods combine statistical tests for out-of-distribution detection.
problem Detecting data points not following the training distribution.
method Combining classical parametric tests (Rao's score test) and a typicality test.
result Combining Fisher's method of test statistics improves out-of-distribution detection accuracy.
Novel method discovers causal relations in time series data, even with autocorrelation.
problem Discovering causal relations in time series data with strong autocorrelation.
method Conditional independence (CI) based PCMCI method, optimized for contemporaneous and lagged links.
result PCMCI outperforms other methods in detecting causal links and controlling false positives.
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.
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernelsstat.ME
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.
SI-CLAD improves clustering-based anomaly detection by controlling false positives.
problem Lack of reliability in clustering-based anomaly detection.
method SI-CLAD (Statistical Inference for CLustering-based Anomaly Detection) using Selective Inference framework.
result SI-CLAD rigorously controls false detection probability below a specified significance level.
In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high di…
Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses , Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…
We present MRPC, an R package that learns causal graphs with improved accuracy over existing packages, such as pcalg and bnlearn. Our algorithm builds on the powerful PC algorithm, the canonical algorithm in computer science for learning directed acyclic graphs. The improvement in accuracy results from online control o…
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis infeasible, they provide a suitable playground for machine learning approaches. Most existing machine learning techniques developed for enviro…
regularized logistic regression has now become a workhorse of data mining and bioinformatics: it is widely used for many classification problems, particularly ones with many features. However, regularization typically selects too many features and that so-called false positives are unavoidable. In this pape…
A new framework detects statistical significance of deep learning in neuroimaging studies.
problem Lack of statistical significance testing in deep learning neuroimaging.
method Non-parametric framework using autoencoders and SVM, with random-effects inference and cross-validation.
result CV and RUB methods offer acceptable false positive rates and statistical power, but low generalization ability.
New method quantifies reliability of neural network image segmentation.
problem Assessing statistical reliability of neural network-based image segmentation results.
method Selective inference framework to compute exact p-values for DNN-driven hypotheses.
result Proposed method successfully controls false positive rate and provides good results for medical image data.
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.
We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces. Our contribution is a new test statistic based on samples from a generative adversarial network designed to approximate directly a conditional distribution that encodes the null hypothesis, i…
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on weakly supervised localization (WSL) where a model is trained to classify an image and localize regi…
Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted M…
Study uses RNN to detect CPs with SI to control false positives.
problem Detecting false positives in RNN-based change point detection.
method Introduces Selective Inference (SI) framework to RNN for CP detection.
result Valid p-values for CPs detected by RNN reduce false positives.
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.
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.
Paper introduces a statistical framework for watermarking LLM-generated text.
problem Detecting LLM-generated text from human-written text with statistical signals.
method Hypothesis testing formulation, pivotal statistic selection, secret key, closed-form expression of false negative rate, minimax optimization.
result Derives optimal detection rules for LLM-generated text, demonstrating higher power than existing methods.
Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples (). In order to make informative inference, we assume that the model is approximately sparse, that is th…
Boosting theory extended to handle cost-sensitive and multi-objective losses.
problem Real-world prediction problems with different error costs.
method Developed a comprehensive theory of cost-sensitive and multi-objective boosting.
result Established a dichotomy for binary classification and a more intricate landscape for multiclass settings.
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Ratesstat.ME
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.
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic genera…
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks f…