Selective inference using randomized group lasso estimators for general models

• URL: http://arxiv.org/abs/2306.13829v3
• Date: Tue, 26 Mar 2024 23:39:35 GMT
• Title: Selective inference using randomized group lasso estimators for general models
• Authors: Yiling Huang, Sarah Pirenne, Snigdha Panigrahi, Gerda Claeskens,
• Abstract summary: The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data. A randomized group-regularized optimization problem is studied. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume.
• Score: 3.4034453928075865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data, for example, and allows for categorical or grouped covariates as well as continuous covariates. A randomized group-regularized optimization problem is studied. The added randomization allows us to construct a post-selection likelihood which we show to be adequate for selective inference when conditioning on the event of the selection of the grouped covariates. This likelihood also provides a selective point estimator, accounting for the selection by the group lasso. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume. The selective inference method for grouped lasso is illustrated on data from the national health and nutrition examination survey while simulations showcase its behaviour and favorable comparison with other methods.

Related papers

• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• BRIO: Bringing Order to Abstractive Summarization [107.97378285293507]
  We propose a novel training paradigm which assumes a non-deterministic distribution. Our method achieves a new state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07 ROUGE-1) datasets.
  arXiv  Detail & Related papers  (2022-03-31T05:19:38Z)
• Black-box Selective Inference via Bootstrapping [5.960626580825523]
  Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso. This work addresses this challenge by introducing a generic approach to estimate the selection event, facilitating feasible inference conditioned on the selection event.
  arXiv  Detail & Related papers  (2022-03-28T05:18:21Z)
• Parameter selection in Gaussian process interpolation: an empirical study of selection criteria [0.0]
  This article revisits the fundamental problem of parameter selection for Gaussian process. We show that the choice of an appropriate family of models is often more important than the choice of a particular selection criterion.
  arXiv  Detail & Related papers  (2021-07-13T11:57:56Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• Goal-oriented adaptive sampling under random field modelling of response probability distributions [0.6445605125467573]
  We consider cases where the spatial variation of response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions.
  arXiv  Detail & Related papers  (2021-02-15T15:55:23Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Inference post Selection of Group-sparse Regression Models [2.1485350418225244]
  Conditional inference provides a rigorous approach to counter bias when data from automated model selections is reused for inference. We develop in this paper a statistically consistent Bayesian framework to assess uncertainties within linear models. Finding wide applications when genes, proteins, genetic variants, neuroimaging measurements are grouped respectively by their biological pathways, molecular functions, regulatory regions, cognitive roles, these models are selected through a useful class of group-sparse learning algorithms.
  arXiv  Detail & Related papers  (2020-12-31T15:43:26Z)
• Selective Classification Can Magnify Disparities Across Groups [89.14499988774985]
  We find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities. Increasing abstentions can even decrease accuracies on some groups. We train distributionally-robust models that achieve similar full-coverage accuracies across groups and show that selective classification uniformly improves each group.
  arXiv  Detail & Related papers  (2020-10-27T08:51:30Z)
• Robust Grouped Variable Selection Using Distributionally Robust Optimization [11.383869751239166]
  We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations. We prove probabilistic bounds on the out-of-sample loss and the estimation bias, and establish the grouping effect of our estimator. We show that our formulation produces an interpretable and parsimonious model that encourages sparsity at a group level.
  arXiv  Detail & Related papers  (2020-06-10T22:32:52Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.