Tootfinder

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
Kevin Chalmers, Jan B{\ae}kgaard Pedersen
https://arxiv.org/abs/2510.10818

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
We present the first spin-free, kernel-lock-free mutex that cooperates with user-mode schedulers and is formally proven FIFO-fair and linearizable using CSP/FDR. Our fairness oracle and stability-based proof method are reusable across coroutine runtime designs. We designed the claim/release protocol for a process-oriented language -- ProcessJ -- to manage the race for claiming shared inter-process communication channels. Internally, we use a lock-free queue to park waiting processes for gaining…

Membership Inference Attacks with False Discovery Rate Control
Chenxu Zhao, Wei Qian, Aobo Chen, Mengdi Huai
https://arxiv.org/abs/2508.07066 https://arxiv…

Membership Inference Attacks with False Discovery Rate Control
Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have been proposed. Despite the significance and popularity of MIAs, existing works on MIAs are limited in providing guarantees on the false discovery rate (FDR), which refers to the expected proportion of false discoveries among the identified p…

Multi-Condition Conformal Selection
Qingyang Hao, Wenbo Liao, Bingyi Jing, Hongxin Wei
https://arxiv.org/abs/2510.08075 https://arxiv.org/pdf/2510.08075

Multi-Condition Conformal Selection
Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignment of large language models. While conformal selection methods offer a rigorous solution with False Discovery Rate (FDR) control, their applicability is confined to single-threshold scenarios (i.e., y > c) and overlooks practical needs for multi-condition selection, such as conjunctive or disjunctive conditions. In th…

A century ago, the government hired unemployed young men to build America's forests, trails, and parks. Photos show FDR's 'tree army.' - Business Insider
https://www.businessinsider.com/civilian-conservation-corps-ccc-fdr-parks-forests-photos

But the Democratic party still at heart seeks to be a progressive party in the FDR/LBJ mold. The party’s gambit since Nixon has been that an FDR-shaped coalition would force the fascist-shape coalition to fracture. It looked like that might even be true for a few of the Obama years. Sadly, nope.
6/

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Chenxin Jiang, Changhu Wang, Jingyi Jessica Li
https://arxiv.org/abs/2507.20598

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (FDR) control with statistical power. Parametric methods such as DESeq2 and edgeR achieve high power by modeling gene-level counts using negative binomial distributions and applying empirical Bayes shrinkage. However, these methods may suffer from FDR inflation when model assumptions are mildly viola…

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
Yuxuan Tao, Adel Javanmard
https://arxiv.org/abs/2508.04800 https://arxiv.org/…

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discovery Rate (FDR) control under differential privacy constraints. While the Model-X knockoff procedure provides FDR guarantees by constructing provably exchangeable ``negative control" features, existing privacy mechanisms like Laplace or Gaussian noise injection disrupt its core exchangeability conditions. Our key innovation lies in privatizing the data knock…

De novo peptide sequencing rescoring and FDR estimation with Winnow
Amandla Mabona, Jemma Daniel, Henrik Servais Janssen Knudsen, Rachel Catzel, Kevin Michael Eloff, Erwin M. Schoof, Nicolas Lopez Carranza, Timothy P. Jenkins, Jeroen Van Goey, Konstantinos Kalogeropoulos
https://arxiv.org/abs/2509.24952

De novo peptide sequencing rescoring and FDR estimation with Winnow
Machine learning has markedly advanced de novo peptide sequencing (DNS) for mass spectrometry-based proteomics. DNS tools offer a reliable way to identify peptides without relying on reference databases, extending proteomic analysis and unlocking applications into less-charted regions of the proteome. However, they still face a key limitation. DNS tools lack principled methods for estimating false discovery rates (FDR) and instead rely on model-specific confidence scores that are often miscalib…

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Ryan Warnick
https://arxiv.org/abs/2508.01988 https://

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Aleatoric and Epistemic uncertainty have achieved recent attention in the literature as different sources from which uncertainty can emerge in stochastic modeling. Epistemic being intrinsic or model based notions of uncertainty, and aleatoric being the uncertainty inherent in the data. We propose a novel decision theoretic framework for outlier detection in the context of aleatoric uncertainty; in the context of Bayesian modeling. The model incorporates bayesian false discovery rate control for…

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza
https://arxiv.org/abs/2508.03314 htt…

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
The dual formulation of empirical risk minimization with f-divergence regularization (ERM-fDR) is introduced. The solution of the dual optimization problem to the ERM-fDR is connected to the notion of normalization function introduced as an implicit function. This dual approach leverages the Legendre-Fenchel transform and the implicit function theorem to provide a nonlinear ODE expression to the normalization function. Furthermore, the nonlinear ODE expression and its properties provide a compu…

False discovery rate control with compound p-values
Rina Foygel Barber, Richard J Samworth
https://arxiv.org/abs/2507.21465 https://arxiv.org/pdf/2507.2146…

False discovery rate control with compound p-values
In the setting of multiple testing, compound p-values generalize p-values by asking for superuniformity to hold only \emph{on average} across all true nulls. We study the properties of the Benjamini--Hochberg procedure applied to compound p-values. Under independence, we show that the false discovery rate (FDR) is at most $1.93α$, where $α$ is the nominal level, and exhibit a distribution for which the FDR is $\frac{7}{6}α$. If additionally all nulls are true, then the upper bound can be imp…

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
Lin Lu, Yuyang Huo, Haojie Ren, Zhaojun Wang, Changliang Zou
https://arxiv.org/abs/2509.03297 …

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
We study online multiple testing with feedback, where decisions are made sequentially and the true state of the hypothesis is revealed after the decision has been made, either instantly or with a delay. We propose GAIF, a feedback-enhanced generalized alpha-investing framework that dynamically adjusts thresholds using revealed outcomes, ensuring finite-sample false discovery rate (FDR)/marginal FDR control. Extending GAIF to online conformal testing, we construct independent conformal $p$-value…

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
Daofu Zhang, Mehrdad Pournaderi, Hanne M. Clifford, Yu Xiang, Pramod K. Varshney
https://arxiv.org/abs/2510.00463

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior under adversarial perturbations remains unexplored. We first formulate an oracle attack setting that quantifies the worst-case degradation of FDR, deriving an upper bound that…

Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control for the filtered rejection set
Marina Bogomolov, Shinjini Nandi
https://arxiv.org/abs/2509.15444

Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control for the filtered rejection set
Modern biological studies often involve testing many hypotheses organized in a group or a hierarchical structure, such as a directed acyclic graph (DAG). In these studies, researchers often wish to control the false discovery rate (FDR) after filtering the discoveries to obtain interpretable results. For addressing this goal, Katsevich, Sabatti, and Bogomolov (2023, Journal of the American Statistical Association, 118(541), 165-176) developed a general method, Focused BH, that guarantees FDR co…

An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Byeonghee Lee, Joonsung Kang
https://arxiv.org/abs/2509.10527 https://

An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate perform…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- De novo peptide sequencing rescoring and FDR estimation with Winnow
Mabona, Daniel, Knudsen, Catzel, Eloff, Schoof, Carranza, Jenkins, Van Goey, Kalogeropoulos

Conformalized Multiple Testing under Unknown Null Distribution with Symmetric Errors
Yang Tian, Zinan Zhao, Wenguang Sun
https://arxiv.org/abs/2509.04231 https://

Conformalized Multiple Testing under Unknown Null Distribution with Symmetric Errors
This article addresses a fundamental concern, first raised by Efron (2004), regarding the selection of null distributions in large-scale multiple testing. In modern data-intensive applications involving thousands or even millions of hypotheses, the theoretical null distribution of the test statistics often deviates from the true underlying null distribution, severely compromising the false discovery rate (FDR) analysis. We propose a conformalized empirical Bayes method using self-calibrated emp…

Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Leveraging the group structure of hypotheses for more powerful multiple testing with FDR control ...
Marina Bogomolov, Shinjini Nandi

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
Heng Ge, Qing Lu
https://arxiv.org/abs/2510.01418 https://arxiv.org/pdf/2510.0…

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
We introduce DiffKnock, a diffusion-based knockoff framework for high-dimensional feature selection with finite-sample false discovery rate (FDR) control. DiffKnock addresses two key limitations of existing knockoff methods: preserving complex feature dependencies and detecting non-linear associations. Our approach trains diffusion models to generate valid knockoffs and uses neural network--based gradient and filter statistics to construct antisymmetric feature importance measures. Through simu…

Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing
Ziyu Xu, Aldo Solari, Lasse Fischer, Rianne de Heide, Aaditya Ramdas, Jelle Goeman
https://arxiv.org/abs/2509.02517

Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing
We present a novel necessary and sufficient principle for multiple testing methods controlling an expected loss. This principle asserts that every such multiple testing method is a special case of a general closed testing procedure based on e-values. It generalizes the Closure Principle, known to underlie all methods controlling familywise error and tail probabilities of false discovery proportions, to a large class of error rates -- in particular to the false discovery rate (FDR). By writing e…

A powerful procedure that controls the false discovery rate with directional information
Zhaoyang Tian, Kun Liang, Pengfei Li
https://arxiv.org/abs/2507.15631

A powerful procedure that controls the false discovery rate with directional information
In many multiple testing applications in genetics, the signs of test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are $p$-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. We…

False Discovery Rate Control via Bayesian Mirror Statistic
Marco Molinari, Magne Thoresen
https://arxiv.org/abs/2510.00875 https://arxiv.org/pdf/2510.00875…

False Discovery Rate Control via Bayesian Mirror Statistic
Simultaneously performing variable selection and inference in high-dimensional models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while being able to control some form of selection error. In this work we adapt the Mirror Statistic approach to False Discovery Rate (FDR) control into a Bayesia…