Conformal Classification with Equalized Coverage for Adaptively Selected Groups

• URL: http://arxiv.org/abs/2405.15106v2
• Date: Wed, 30 Oct 2024 05:52:09 GMT
• Title: Conformal Classification with Equalized Coverage for Adaptively Selected Groups
• Authors: Yanfei Zhou, Matteo Sesia,
• Abstract summary: This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. We demonstrate the validity and effectiveness of this method on simulated and real data sets.
• Score: 9.016173836219524
• License:
• Abstract: This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. These features are carefully selected to reflect potential model limitations or biases. This can be useful to find a practical compromise between efficiency -- by providing informative predictions -- and algorithmic fairness -- by ensuring equalized coverage for the most sensitive groups. We demonstrate the validity and effectiveness of this method on simulated and real data sets.

Related papers

• Noise-Adaptive Conformal Classification with Marginal Coverage [53.74125453366155]
  We introduce an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise. We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets.
  arXiv  Detail & Related papers  (2025-01-29T23:55:23Z)
• Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
  It is impossible to achieve exact, distribution-free conditional coverage in finite samples. We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
  arXiv  Detail & Related papers  (2025-01-17T12:01:56Z)
• Adaptive Conformal Inference by Betting [51.272991377903274]
  We consider the problem of adaptive conformal inference without any assumptions about the data generating process. Existing approaches for adaptive conformal inference are based on optimizing the pinball loss using variants of online gradient descent. We propose a different approach for adaptive conformal inference that leverages parameter-free online convex optimization techniques.
  arXiv  Detail & Related papers  (2024-12-26T18:42:08Z)
• Multi-model Ensemble Conformal Prediction in Dynamic Environments [14.188004615463742]
  We introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected on the fly from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage.
  arXiv  Detail & Related papers  (2024-11-06T05:57:28Z)
• Weighted Aggregation of Conformity Scores for Classification [9.559062601251464]
  Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees. We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors.
  arXiv  Detail & Related papers  (2024-07-14T14:58:03Z)
• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• The Penalized Inverse Probability Measure for Conformal Classification [0.5172964916120902]
  The work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness. The work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency.
  arXiv  Detail & Related papers  (2024-06-13T07:37:16Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• Classification with Valid and Adaptive Coverage [11.680355561258427]
  Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm. We develop specialized versions of these techniques for categorical and unordered response labels.
  arXiv  Detail & Related papers  (2020-06-03T21:42:04Z)
• Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation [74.06904875527556]
  We propose a generic and efficient segmentation framework to construct ensemble segmentation models. In the proposed method, ensemble models can be efficiently generated by using the layer selection method. We also devise a new pixel-wise uncertainty loss, which improves the predictive performance.
  arXiv  Detail & Related papers  (2020-05-21T16:08:38Z)

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.