Modeling true and false news diffusion in social networks using homogeneity.
problem Difficulties in distinguishing true from false news in social networks.
method Proposes a Bayesian nonparametric model that incorporates homogeneity of news stories to predict their genuineness.
result Homogeneity values of news stories strongly correlate with their genuineness and content.
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.
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.
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.
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Paper is withdrawn due to errors (superseded by math.AG/0604303). Formula 6.5 is false. Section 6 is false, and the main statement is true only for bundles B with c1(B) SU(2)-invariant.
Paper withdrawn due to errors (superseded by math.AG/0604303). Proposition 11.4 is false, Section 12 is false, and the main statement is true only for bundles B with c1(B) SU(2)-invariant.
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.
Simple bounds show most cross-sectional predictability findings are likely true.
problem Determining the validity of cross-sectional return predictability findings.
method Developed simple and intuitive bounds on the false discovery rate (FDR).
result Bounds show the FDR is small, indicating most findings are likely true.
Proposes a method to handle sparse multiway count data with false zeros using zero-truncated Poisson regression.
problem Handling sparse multiway count data corrupted by false zeros.
method Zero-truncated Poisson regression with tensor completion.
result Accurate estimation of multiway count data from approximately IR2log22(I) non-zero counts. We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certain conditions. Our general approach is to adapt a well-known variant of the Benjamini-Hochberg procedure (BHq), making each step differential…
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.
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.
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false. We demonstrate here that this assertion in [7] is false, and is based on a misreading of the notion of set membership in [13, Proposition 11]. We maintain that [13, Proposition 11] is true. ([7] = arXiv:1809.00593, [13] = arXiv:15…
Study examines flaws in probing LLMs' knowledge and introduces a new method.
problem Flaws in existing methods for probing the veracity of LLMs' internal knowledge.
method sAwMIL (Sparse-Aware Multiple-Instance Learning) combining multiple-instance learning with conformal prediction.
result LLMs encode a third type of signal distinct from true and false.
Proposes a Dual Focal Loss method to improve semantic segmentation on unbalanced datasets.
problem Improving performance on unbalanced datasets in semantic segmentation.
method Adaptive Class Weight based Dual Focal Loss (DFL) function.
result DFL improves convergence and reduces class imbalance effect.
A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
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…
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.
SWA selects important features from large data sets, controlling false discovery rate.
problem Feature selection in large regression data, especially scaling to big data and matching target FDR.
method Subsampling Winner algorithm using subsampling and scoring features.
result SWA controls actual FDR better than benchmark procedures and randomForest.
Method quantifies uncertainty in full ranking with new CP approach.
problem Uncertainty quantification in full ranking with unknown ground truth.
method Transductive Conformal Prediction (CP) method to construct distribution-free bounds.
result Valid prediction sets and false coverage proportion control for full ranking.
AdaDetectGPT improves text authorship detection with statistical guarantees.
problem Determining if text is authored by a human or an LLM.
method Adaptive learning of a witness function from training data to enhance logits-based detectors.
result AdaDetectGPT nearly uniformly improves text authorship detection, with up to 37% improvement.
Study explores rumor spread on Twitter using supervised learning.
problem Rumors spread quickly on social networks, affecting decision-making.
method Microscopic-level analysis of user interactions and features.
result Models predict rumor diffusion with 90% accuracy.
J.H.C. Whitehead defined a map Jr:πr(SO)→πrs from the homotopy of the special orthogonal group to the stable homotopy of spheres. Within a toy model we show how the known computation for kernel(J) leads to nonlinear σ-models with spherical source (space) and spherical target which admit false vacua…
A compactum X is an `absolute cone' if, for each of its points x, the space X is homeomorphic to a cone with x corresponding to the cone point. In 1971, J. de Groot conjectured that each n-dimensional absolute cone is an n-cell. In this paper, we give a complete solution to that conjecture. In particular, we show that …
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.
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…
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.
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.
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.
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.
New optimization method improves AUC for binary classification and changepoint detection.
problem Non-convex AUC and sub-optimal points in ROC curves.
method AUM (Area Under Min(FP, FN)) surrogate loss function based on sorting and summing ROC curve points.
result AUM minimization learning algorithm improves AUC and speeds up compared to previous methods.
Akbulut and Kirby conjectured that two knots with the same 0-surgery are concordant. In this paper, we prove that if the slice-ribbon conjecture is true, then the modified Akbulut-Kirby's conjecture is false. We also give a fibered potential counterexample to the slice-ribbon conjecture.
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.
Proposes a new method for generating better negative examples in KBC.
problem Random negative sampling generates nonsensical examples that slow down learning and decrease accuracy.
method Distributional Negative Sampling generates meaningful negative examples.
result Significant improvement in Mean Reciprocal Rank values in three benchmarks.
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.
We find necessary and sufficient conditions for a complete n-dimensional Riemannian manifold of finite volume, whose curvature tensor has nullity at least n−2, to be a geometric graph manifold. In the process, we show that Nomizu's conjecture, well known to be false in general, is true for manifolds with finite vol…
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.
PH-CS selects test inputs with reliability guarantees, adapting FDR to data.
problem Fixed FDR limits adaptability to downstream needs.
method Post-hoc conformal selection with e-variables, e-BH procedure.
result PH-CS provides reliable FDP estimates and competitive FDR control.
Paper analyzes privacy risks of learning complex models.
problem Privacy leakage from learning high-dimensional graphical models.
method Theoretical analysis of tracing attacks on Bayesian networks.
result Provides a tight upper bound on tracing attack power.
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…
Enhanced attacks quantify machine learning data leakage.
problem Quantifying how much machine learning models reveal about their training data.
method Hypothesis testing framework for membership inference attacks.
result New attacks achieve higher true positive rates with lower false positive rates.
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 α (e.g., 0.05) while maximizing true positive rate. We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel…
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…
For n >1, if the Seifert form of a knotted 2n-1 sphere K in S^{2n+1} has a metabolizer, then the knot is slice. Casson and Gordon proved that this is false in dimension three (n = 1). However, in the three dimensional case it is true that if the metabolizer has a basis represented by a strongly slice link then K is sli…
Study on Riemannian isoperimetric inequality, finding it true generically.
problem Direct analogue of Euclidean isoperimetric inequality is false on Riemannian manifolds.
method Used Lojasiewicz-Simon inequality for real analytic metrics.
result Modified quantitative isoperimetric inequality holds for real analytic metrics.
Active learning improves anomaly detection by tuning ensembles with true labels.
problem Configuring anomaly detectors for minimal effort on false positives.
method Compact description, data drift detection, active learning strategies.
result Active learning significantly improves anomaly discovery and adapts to streaming data.