Data augmentation improves keyword spotting accuracy in noisy conditions.
problem Maintaining low false reject rates in far-field KWS with playback interference.
method Artificially corrupted training data with mixed music and TV audio.
result 30-45% reduction in false reject rates under audio playback.
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.
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 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.
We propose a linear-time, single-pass, top-down algorithm for multiple testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and edges specify a partial ordering in which hypotheses must be tested. The procedure is guaranteed to reject a sub-DAG with bounded false discovery rate (FDR) while satisf…
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…
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…
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
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.
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.
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.
The paper tackles online FDR control in hypothesis testing with contextual features.
problem Controlling false discoveries in online hypothesis testing with contextual features.
method Proposes a new class of online testing procedures that learn significance levels sequentially, incorporating contextual information and previous results.
result Proves that the proposed procedures control online FDR under standard assumptions and outperform existing methods in terms of statistical power.
In many practical applications of multiple hypothesis testing using the False Discovery Rate (FDR), the given hypotheses can be naturally partitioned into groups, and one may not only want to control the number of false discoveries (wrongly rejected null hypotheses), but also the number of falsely discovered groups of …
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
Optimal regression with reject option using conditional variance thresholding.
problem Regression with reject option to handle uncertain predictions.
method Derive optimal rule based on thresholding conditional variance, semi-supervised estimation using labeled and unlabeled data.
result The predictor with reject option is almost as good as the optimal predictor in terms of risk and rejection rate.
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
New algorithm reduces rejection rate in adaptive rejection sampling.
problem Reducing high rejection rates in adaptive rejection sampling.
method Introduces a minimax near-optimal algorithm for adaptive rejection sampling.
result Guarantees a near-optimal rejection rate in a minimax sense.
ELS framework improves safety alignment by dynamically steering LLMs towards helpful responses.
problem Over-Refusal in Aligned Large Language Models
method Fine-tuning free framework using an Energy-Based Model (EBM) to dynamically steer LLMs during inference.
result Extensive experiments show a significant reduction in false refusals (from 57.3% to 82.6%) while maintaining safety performance.
Marxism's rate of profit thesis rejected as illogical.
problem Marxism's rate of profit thesis is challenged as illogical.
method Rejects Marx's thesis on the long-term tendency of the rate of profit to fall.
result Marxism's rate of profit thesis is rejected as illogical.
PRS improves rejection sampling by learning better proposals.
problem High rejection rate in traditional rejection sampling.
method PRS uses a kernel estimator to learn better sampling proposals.
result PRS guarantees a low number of accepted samples.
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.
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.
Proposes a semi-Bayesian nonparametric estimator for MMD in GOF tests and GANs.
problem Challenges in goodness-of-fit testing for intractable models.
method Semi-Bayesian nonparametric estimator of MMD.
result Outperforms frequentist MMD-based methods in false rejection and acceptance rates.
Paper proposes a method to detect unknown anomalous sounds without training data using deep learning and Neyman-Pearson lemma.
problem Unsupervised detection of unknown anomalous sounds in audio data.
method Uses an autoencoder to minimize reconstruction error of normal sounds and Neyman-Pearson lemma to maximize true positive rate under low false positive rate conditions.
result The proposed method improves performance measures of unsupervised anomaly detection in audio data under low false positive rate conditions.
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 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.
We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test the…
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.
New models extrapolate false alarms in ASV without new data.
problem Reliable extrapolation of false alarm rates in ASV without new speaker data.
method Generative models in ASV score space for arbitrary systems.
result Models accurately extrapolate false alarm rates for large speaker populations.
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.
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…
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.
We introduce a new statistical test of the hypothesis that a balanced panel of firms have the same growth rate distribution or, more generally, that they share the same functional form of growth rate distribution. We applied the test to European Union and US publicly quoted manufacturing firms data, considering functio…
DSDE improves OoD detection by estimating model library proportions.
problem Uncertainty quantification and balanced error rates in model selection for OoD detection.
method Inverted sequential p-value strategies, change-point detection, automatic hyperparameter selection.
result DSDE reduces FPR from 11.07% to 3.31% on CIFAR10.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
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.
Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existi…
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.
Paper achieves fast learning rates without margin assumptions in agnostic settings.
problem Learning fast rates in the agnostic setting without margin assumptions.
method Proposes a specific learning algorithm for classification with a reject option.
result Achieves a learning rate of $O\left(\frac{d}{n}\log \frac{n}{d}
ight)$ in the agnostic setting.
Study compares statistical properties and power of divergence measures for credit risk monitoring.
problem Detecting distributional shifts in credit risk models.
method Derives statistical properties and chi-square benchmark values for Jensen-Shannon Divergence and Kullback-Leibler Divergence, demonstrating their applicability in credit risk monitoring.
result Jensen-Shannon Divergence and Kullback-Leibler Divergence follow chi-square distributions and reveal practical trade-offs in minimizing false positives vs. detecting changes.
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.
The paper introduces false discovery rate control for BMF to avoid noisy patterns.
problem No guarantees exist for BMF patterns being real, not just noise.
method Proposes false discovery rate (FDR) to control BMF patterns, proving bounds on FDR.
result Improved BMF algorithms using theoretical FDR bounds for rank selection.
Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.
problem Inefficiency of Hamiltonian Monte Carlo on ReLU neural networks.
method Analysis of Hamiltonian Monte Carlo with leapfrog integrator for Bayesian neural network inference.
result Leapfrog HMC for ReLU networks has a large local error rate of Ω(ε), leading to inefficiency. SurvNet selects important variables in DNNs with false discovery rate control.
problem Variable selection in deep neural networks (DNNs) for interpretability.
method Backward elimination procedure based on a new variable importance measure.
result SurvNet estimates and controls false discovery rate of selected variables.
A method to control false membership rate in unsupervised mixture models.
problem Controlling misclassification in ambiguous datasets.
method Develops a plug-in procedure with theoretical guarantees on FMR.
result The false membership rate does not exceed the pre-defined nominal level α. Study shows improving information content in risk scores can reduce algorithmic discrimination.
problem Algorithmic prediction systems may discriminate against underrepresented populations.
method Investigates the role of information in fair prediction, introduces refinement concept.
result Improving information content through refinements improves downstream selection rules and reduces disparity in treatment.
Efficient keyword spotting model using dilated convolutions and gating.
problem Keyword spotting in resource-constrained environments.
method End-to-end temporal modeling with dilated convolutions, gated activations, and residual connections.
result Our model outperforms LSTM-based keyword spotting with a significant decrease in false rejection rate.
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.