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.
aLTT selects hyperparameters efficiently with statistical guarantees.
problem Statistical validity and efficiency in hyperparameter selection.
method Sequential data-dependent multiple hypothesis testing with early termination.
result Reduces testing rounds while maintaining statistical validity.
This paper evaluates test selection methods for deep neural networks, revealing their limitations.
problem Challenges in testing deep learning systems due to high labeling costs.
method Analysis and empirical testing of 11 test selection methods on five datasets.
result Test selection methods can fail under certain conditions, leading to significant drops in test relative coverage.
Study provides selective inference method for latent block models.
problem Challenges in constructing a test on a block structure selected by clustering algorithms.
method Developed a selective inference method for latent block models using squared residue minimization and simulated annealing.
result Proposed tests effectively handle selective bias in block structures compared to naive tests.
Regularizing for or against class selectivity in DNNs improves test accuracy.
problem The necessity and sufficiency of class selectivity in DNNs.
method Direct regularization of class selectivity in convolutional neural networks.
result Reducing class selectivity improves test accuracy, while increasing it decreases it.
Selective inference controls Type I error in k-means clustering tests.
problem Inflated Type I error in classical hypothesis tests for k-means clusters.
method Selective inference approach to control Type I error.
result Proposes a computable finite-sample p-value for selective inference.
New framework for valid hypothesis testing in complex data settings.
problem Challenges in classical hypothesis testing frameworks.
method Add and subtract external noise to partition data, orthogonalize, and test hypotheses.
result Valid hypothesis tests can be conducted under minimal assumptions.
Testing of deep learning models is challenging due to the excessive number and complexity of computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of how we can automatically select candidate test data to test deep learning models. Recent research h…
Proposes a modified Morgan-Pitman test for evaluating variances in machine learning models.
problem Limited ability to account for sampling variability in model selection.
method Enhances the classic Morgan-Pitman test for robustness in non-linear models with heavy-tailed distributions or outliers.
result Demonstrates the test's effectiveness and practical utility in model evaluation and selection.
Equivalent tests for SGD batch size selection found.
problem Finding equivalent tests for adaptive batch size selection in SGD.
method Norm and inner product/orthogonality tests equivalence demonstration.
result Norm and inner product/orthogonality tests are equivalent under specific conditions.
Proposes a method to select variables for kernel two-sample tests.
problem Determining whether two samples have the same distribution using informative variables.
method A framework based on kernel maximum mean discrepancy (MMD) for selecting a subset of variables.
result The sample size requirements for the three kernels depend on the number of selected variables, not the data dimension.
Bayesian method improves adaptive testing item selection, ensuring full item exposure.
problem Adaptive testing selects items to estimate ability, but must also ensure diverse item exposure.
method Formulated as Bayesian model averaging, deriving optimal item sampling probabilities.
result Stochastic method achieves full item bank exposure without sacrificing accuracy.
A new method optimizes MMD test power by dynamically selecting kernels, overcoming traditional trade-offs.
problem Fixed kernels fail to distinguish certain distributions, leading to overfitting and variance collapse.
method Complexity-Penalized MMD (CP-MMD) criterion, derived from concentration inequality, optimizes kernel selection.
result CP-MMD maximizes true test power while ensuring unconditional Type-I validity, matching or exceeding state-of-the-art performance.
A new framework selects information sources to test hypotheses robustly, even with misclassifications.
problem Robust hypothesis testing with misclassification penalties.
method Introduces a misclassification penalty framework and an efficient greedy algorithm.
result Proposes a submodular surrogate metric for better selection.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.
The paper develops a test for independence of selected Gaussian variables after thresholding correlations.
problem Testing independence of selected Gaussian variables after thresholding correlations.
method The approach involves conditioning on the selection event and using a new characterization of the conditioning event in terms of canonical correlation.
result The proposed test has higher power than a naive approach that ignores selection effects.
Proposes selective inference for testing differences in means between clusters.
problem Inflated type I error rate when testing differences in means between clusters.
method Selective inference approach to control selective type I error rate.
result Controls selective type I error rate by accounting for data-driven cluster definition.
This paper applies combinatorial testing to machine learning for robust model performance.
problem Identifying robust machine learning models using test and training sets.
method Adapting combinatorial interaction testing for machine learning, focusing on simple features.
result Combinatorial coverage can enhance model performance and robustness.
Many model selection algorithms produce a path of fits specifying a sequence of increasingly complex models. Given such a sequence and the data used to produce them, we consider the problem of choosing the least complex model that is not falsified by the data. Extending the selected-model tests of Fithian et al. (2014)…
Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical analysis. An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate…
Optimal subset selection for hypothesis testing with penalties.
problem Optimal subset selection of information sources for hypothesis testing with misclassification penalties.
method Proposes a misclassification penalty framework and studies two variants of subset selection problems under centralized Bayesian learning.
result Proves the submodularity of the objective and constraints of the subset selection problems and establishes performance guarantees for greedy algorithms.
Bayesian method selects important covariates in modal regression.
problem Bayesian modal regression with heavy-tailed responses.
method Expectation-maximization algorithm for parameter estimation; test statistic for variable selection.
result Efficacy of the proposed method in identifying important covariates.
Hotelling's T2-test for the mean of a multivariate normal distribution is one of the triumphs of classical multivariate analysis. It is uniformly most powerful among invariant tests, and admissible, proper Bayes, and locally and asymptotically minimax among all tests. Nonetheless, investigators often prefer non-inva…
Study proposes a statistical testing framework for evaluating clustering pipelines.
problem Quantifying the statistical reliability of clustering results from data analysis pipelines.
method Selective inference-based statistical testing framework for clustering pipelines.
result The proposed test controls the type I error rate and is effective in validating clustering results.
Develops abstention procedure for nonparametric regression via variance testing.
problem Prediction with selective abstention in error-critical machine learning.
method Nonparametric heteroskedastic regression via testing hypothesis on conditional variance.
result Non-asymptotic risk bounds and convergence regimes for the estimator.
In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sampl…
The paper addresses selection bias in conformal prediction for focal units.
problem Selection bias in marginally valid conformal prediction intervals for focal units.
method A general framework for constructing selection-conditional coverage prediction sets.
result Efficient methods for various selection rules with exact finite-sample coverage.
Statistical guarantees for hyperparameter selection
problem Hyperparameter selection in AI systems
method Learn-then-test framework
result Provable reliability and safety
Algorithm learns from both labeled and arbitrary test examples, giving guarantees for bounded VC dimension classes.
problem Learning from arbitrary test examples, not just perturbations.
method Selective transductive learning algorithm that outputs abstaining predictions.
result Nontrivial guarantees for bounded VC dimension classes with arbitrary train and test distributions.
Novel approach uses neural networks to enhance CI testing for feature selection.
problem Challenges in implementing Markov blanket feature selection due to CI testing limitations.
method Two-step approach: feature mapping followed by CI testing using k-NN. result The method boosts CI testing performance, leading to improved feature selection.
Develops a statistical test for IV, improving feature selection reliability.
problem Lack of statistical justification in conventional IV-based feature selection.
method Establishes connection with Jeffreys divergence and proposes a nonparametric hypothesis test.
result The J-Divergence test provides rigorous guarantees and is more reliable than traditional IV thresholds.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algorithm in high dimensions under the assumption of Gaussian noise. LARS is known to produce a piecewise affine solution path with change points referred to as the knots of the LARS path. The key to our results is an express…
A new method for kernel tests without data splitting increases power.
problem Lack of power in kernel-based tests due to data splitting.
method Selective inference framework to learn hyperparameters and test on full sample.
result Empirically larger test power without data splitting, regardless of split proportion.
Paper proposes SCQ and P-TAMS for structured OOD testing in high-stakes ML.
problem Difficulty of incorporating auxiliary information in traditional conformal methods.
method Structure-adaptive conformal q-value (SCQ) and pseudo-score-guided transductive automated model selection (P-TAMS).
result Unified framework controls false discovery rate and improves power across diverse settings.
New framework minimizes interference and selection bias in network A/B testing.
problem Interference and selection bias in network A/B testing.
method Proposes a principled framework that jointly minimizes interference and selection bias using edge spillover probability and cluster matching.
result Significantly lower error in causal effect estimation compared to existing solutions.
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 α (e.g., 0.05) and enhances statistical power. Extends knockoff filter for composite null hypotheses in variable selection.
problem Handling composite null hypotheses in variable selection.
method Developed two methods for composite inference with knockoffs: S-OLS and FRPP.
result Proposed heuristic variants of S-OLS outperforming BH procedure for composite nulls.
ECCIT improves conditional independence tests by calibrating for miscalibration.
problem Inaccurate frequentist guarantees in CITs, especially in small samples and misspecified models.
method Empirically Calibrated Conditional Independence Tests (ECCIT) that optimize and correct for miscalibration.
result ECCIT achieves valid FDR with higher power than existing calibration strategies.
Stable health predictions need deconfounding test set features.
problem Stability of predictions in health machine learning is compromised by selection biases.
method Deconfounding the test set features improves prediction stability across different environments.
result Improved stability achieved by deconfounding test set features.
DRCS selects a subset of data to minimize worst-case test error under covariate shift.
problem Selecting a subset of data that performs well across different deployment scenarios when data distributions differ.
method DRCS derives an upper bound for the worst-case test error assuming covariate shift and selects instances to minimize this bound.
result DRCS achieves distributionally robust training instance selection.
New method selects best HTE estimator without ground-truth treatment effects.
problem Selecting best HTE estimator from multiple candidates.
method Cross-fitted, exponentially weighted test statistic with two-way sample splitting.
result Empirically, reliable error control and reduced false selections.
Model selection for time series forecasting can be biased by the distribution of scores.
problem Model selection for probabilistic forecasting on time series data.
method Using proper scoring rules to aggregate scores across multiple time series.
result The mean score is immune to the skewness of the score distribution.
A/B testing improves marketing decisions by selecting effective stratification variables.
problem Improving the sensitivity of A/B testing through stratified sampling.
method Designing an algorithm to select a subset of stratification variables for variance reduction.
result The subset selection method outperforms other variance reduction techniques in A/B testing.
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent network models are studied both analytically and experimentally using this test. Our results suggest that in general there exists a conflict be…
Two statistical tasks are shown to have equivalent sample complexity.
problem Determining if a function depends on only a few variables and identifying those variables.
method Proved statistical equivalence of feature selection and junta testing through sample complexity analysis.
result Brute-force algorithm is sample-optimal for both tasks with optimal sample size.
EASE optimizes exemplar selection for ICL in LLMs efficiently.
problem Efficiently selecting exemplars for in-context learning (ICL) in large language models (LLMs).
method EASE uses neural bandit algorithms to optimize exemplar sets, considering both exemplar ordering and instruction.
result EASE outperforms existing methods in finding optimal exemplar sets for all test queries.
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection fo…