New method estimates selection event for black-box models.
problem Infeasibility of conditional selective inference due to unavailable selection event.
method Bootstrapping to estimate selection event and conditional distribution.
result Feasibility of conditional selective inference for models without exact selection event.
Exact selective inference with randomization for Gaussian regression models.
problem Exact selective inference in Gaussian regression models.
method Introduces a pivot for exact selective inference with randomization, reducing the problem to a bivariate truncated Gaussian distribution.
result Our pivot leads to exact inference and produces narrower confidence intervals than related methods.
Proposes HSIC-Lasso for selective inference in non-linear data.
problem Detecting influential features in non-linear and high-dimensional data.
method Model-free HSIC-Lasso based on truncated Gaussians and polyhedral lemma.
result Tight control of type-I error even for small sample sizes.
Flexible selective inference using flow-based transport maps.
problem Selective inference with complex selection events.
method Flow-based generative modeling for conditional distribution approximation.
result Valid p-values and confidence sets for adaptively selected hypotheses and parameters.
New method corrects selection bias in post-selective inference for Group LASSO.
problem Inference after Group LASSO selection is unreliable.
method Develops a consistent, post-selective Bayesian method to adjust for selection bias.
result Corrects bias in recovering effects of selected variables.
Develops a more powerful selective inference method for stepwise feature selection.
problem Loss of power in existing conditional SI methods due to over-conditioning.
method Uses homotopy continuation approach to overcome over-conditioning.
result Shows improved power and efficiency in selective inference for feature selection.
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.
Selective inference for group lasso estimators across various distributions and covariates.
problem Developing selective inference methods for group lasso estimators.
method Randomized group-regularized optimization problem with post-selection likelihood.
result Selective point estimator and Wald-type confidence regions for regression parameters.
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
Paper improves Lasso for S&P500 index tracking with post-selection inference.
problem Index tracking for S&P500 with many applications.
method Used Lasso for dimension reduction and post-selection inference.
result Lasso method for S&P500 index tracking shows high performance.
Study examines inference methods after variable selection in Cox models.
problem Bias and misleading inference after variable selection in Cox models.
method Simulation study of inference procedures for Lasso and adaptive Lasso in Cox models.
result Performance of inference procedures varies, with debiased Lasso showing promise.
Proposes a new method to improve selective inference for Lasso models.
problem Over-conditioning due to conditioning on feature signs in selective inference for Lasso.
method Parametric programming approach to avoid conditioning on signs and identify feature selection events.
result Improves power and practicality of selective inference for Lasso models.
Enhances selective inference for generalized lasso using parametric programming.
problem Low statistical power in selective inference for generalized lasso.
method Parametric programming to compute solution paths and identify model selection events.
result Improves selective inference power and practicality for various problems.
A new method for efficient inference and model selection in SBMs using OT.
problem Efficient inference and model selection in stochastic block models.
method Interpreting MLVI as srGW with entropic regularization, then unregularizing for sparse solutions, and adding a sparsity-promoting regularizer.
result The method consistently recovers SBM parameters and selects the number of clusters in finite samples.
Selective inference framework for CART trees to control error rates and coverage.
problem Inference on CART trees does not control Type 1 error rates and coverage.
method Selective inference framework conditioning on tree estimation, efficient algorithms.
result Proposes tests and intervals for CART trees with selective error control.
New method corrects selection bias in complex models.
problem Selection bias in statistical studies leading to systematic distortions.
method Amortized Bayesian inference with neural posterior estimation.
result Recover well-calibrated posterior distributions across diverse selection mechanisms.
Selective inference improves multi-task neuroimaging analysis.
problem Improving predictive performance and modeling accuracy in neuroimaging studies.
method Proposes a framework for selective inference to jointly identify relevant covariates and conduct valid inference in a sparsity-inducing model.
result Selective inference yields tighter confidence intervals and more accurate signal recovery than single-task methods.
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.
si4onnx enables selective inference on deep learning models.
problem Establishing the reliability of AI systems through statistical significance of identified regions.
method Selective inference techniques implemented through a Python package.
result Controlled type I error rates for hypothesis testing on deep learning models.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly contro…
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…
This thesis studies two problems in modern statistics. First, we study selective inference, or inference for hypothesis that are chosen after looking at the data. The motiving application is inference for regression coefficients selected by the lasso. We present the Condition-on-Selection method that allows for valid s…
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.
Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In t…
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
The paper discusses selecting predictive models for causal inference, highlighting the challenges and proposing a solution.
problem Selecting the best predictive models for causal inference from a variety of machine learning models.
method The paper proposes using Rext−risk, flexible estimators, and splitting data to compute risks for model selection. result The proposed method controls both outcome errors for treated and non-treated individuals, addressing the issue of model selection for causal inference.
New method reduces computational cost for selective inference.
problem Over-conditioning in selective inference.
method Parametric programming-based selective inference (PP-based SI) with bounded p-values.
result Reduced computational cost while maintaining desired precision.
OnlineSCI extends ACI for adaptive selective inference with improved coverage and IER control.
problem Adaptive selective inference in online settings with improved coverage and IER control.
method Adaptive selective inference with extended ACI algorithm.
result OnlineSCI controls average missed coverage and instantaneous error rate at selected times, up to a non-asymptotic remainder term.
Paper proposes a new method for selective inference in robust regression.
problem Statistical inference after removing outliers identified by robust methods.
method Conditional SI using piecewise-linear homotopy continuation.
result Proposed method is applicable to a wide class of robust regression and outlier detection methods.
Exact inference method for Wasserstein distance with finite-sample coverage.
problem Asymptotic approximation methods for Wasserstein distance lack finite-sample validity.
method Selective Inference inspired approach for exact inference.
result Valid confidence interval for Wasserstein distance with finite-sample coverage.
Bayesian coresets improve scalable Bayesian inference.
problem Efficiently approximating posterior inference with a subset of data.
method Sparsity constrained optimization and accelerated optimization methods.
result Explicit convergence rate guarantees and superior performance compared to state-of-the-art.
OptCS optimizes model selection after conformal inference, controlling FDR and power loss.
problem Challenges in model selection for conformal inference, especially when limited labeled data and many model choices are available.
method OptCS framework that allows valid statistical testing after flexible data-driven model optimization, using novel multiple testing procedures.
result Valid conformal p-values constructed despite substantial data reuse, maintaining FDR control.
SEISM tests neural network features for regulatory genomics.
problem Testing neural network features for regulatory genomics.
method Selective inference procedure for sequence motifs.
result Sampling under specific parameters characterizes composite null hypothesis.
Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.
problem Interpreting causal effect moderation in high-dimensional data with interpretability and avoiding false positives.
method Two-step method: 1) Selects a smaller model for linear causal effect moderation using Gaussian randomization, 2) Conditions on selection to construct a pivot for uniformly asymptotic semi-parametric inference.
result Consistently achieves valid coverage rates and shorter, bounded intervals in time-varying causal effect moderation.
BayesBoost combines boosting and Bayesian methods for linear mixed models, improving uncertainty estimation and variable selection.
problem Lack of straightforward uncertainty estimation for parameters in high-dimensional linear mixed models.
method BayesBoost: Combines boosting and Bayesian inference for linear mixed models.
result Improves uncertainty estimation and variable selection in linear mixed models.
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.
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random…
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that da…
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.
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.
Unified framework for causal inference under sample selection.
problem Causal inference under sample selection with treatment and outcome non-randomness.
method ForestRiesz estimator, Riesz representation framework.
result ForestRiesz estimator yields more stable treatment effect estimates than conventional double machine learning approaches.
Cross-balancing improves causal inference by balancing features with outcome data.
problem Balancing features for valid causal inference when outcome data is available.
method Cross-balancing using sample splitting to separate feature construction and weight estimation errors.
result Cross-balancing produces consistent, asymptotically normal, and efficient estimators under mild conditions.
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.
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.
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.
Develops methods to adjust prediction set coverage based on post-selection analysis.
problem Adjusting prediction set coverage after initial analysis to better fit specific needs.
method Post-selection conformal inference to adjust miscoverage levels.
result Allows for trade-off between coverage and prediction set quality.
AMES framework selects optimal embedding space for latent graph inference.
problem No principled method for choosing the best embedding space for latent graph inference.
method Differentiable AMES framework using backpropagation to select optimal embedding space.
result Consistently achieves comparable or superior results across multiple datasets.