Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation

• URL: http://arxiv.org/abs/2405.16246v2
• Date: Wed, 29 Jan 2025 23:46:43 GMT
• Title: Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
• Authors: Eduardo Ochoa Rivera, Yash Patel, Ambuj Tewari,
• Abstract summary: Conformal prediction is one approach that avoids distributional assumptions.<n>Merging prediction regions directly, however, sacrifices structures present in the conformal scores that can further reduce conservatism.<n>We show that a novel framework can be efficiently leveraged in both classification and predict-then-optimize regression settings.
• Score: 18.928543069018865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distribution-free uncertainty estimation for ensemble methods is increasingly desirable due to the widening deployment of multi-modal black-box predictive models. Conformal prediction is one approach that avoids such distributional assumptions. Methods for conformal aggregation have in turn been proposed for ensembled prediction, where the prediction regions of individual models are merged as to retain coverage guarantees while minimizing conservatism. Merging the prediction regions directly, however, sacrifices structures present in the conformal scores that can further reduce conservatism. We, therefore, propose a novel framework that extends the standard scalar formulation of a score function to a multivariate score that produces more efficient prediction regions. We then demonstrate that such a framework can be efficiently leveraged in both classification and predict-then-optimize regression settings downstream and empirically show the advantage over alternate conformal aggregation methods.

Related papers

• Minimum Volume Conformal Sets for Multivariate Regression [44.99833362998488]
  Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. We propose an optimization-driven framework based on a novel loss function that directly learns minimum-conformity covering sets. Our approach optimize over prediction sets defined by arbitrary norm balls, including single and multi-norm formulations.
  arXiv  Detail & Related papers  (2025-03-24T18:54:22Z)
• E-Values Expand the Scope of Conformal Prediction [49.1574468325115]
  Conformal prediction is a powerful framework for distribution-free uncertainty quantification. In this paper, we explore an alternative approach based on e-values, known as conformal e-prediction. E-values offer key advantages that cannot be achieved with p-values, enabling new theoretical and practical capabilities.
  arXiv  Detail & Related papers  (2025-03-17T10:54:30Z)
• Rectifying Conformity Scores for Better Conditional Coverage [75.73184036344908]
  We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage.
  arXiv  Detail & Related papers  (2025-02-22T19:54:14Z)
• Epistemic Uncertainty in Conformal Scores: A Unified Approach [2.449909275410288]
  Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture uncertainty. We introduce $texttEPICSCORE$, a model-agnostic approach that enhances any conformal score by explicitly integrating uncertainty. $texttEPICSCORE$ adaptively expands predictive intervals in regions with limited data while maintaining compact intervals where data is abundant.
  arXiv  Detail & Related papers  (2025-02-10T19:42:54Z)
• 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)
• Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series [7.200880964149064]
  Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models. Applying conformal prediction to probabilistic generative models, such as Normalising Flows is not straightforward. This work proposes a novel method to conformalise conditional normalising flows, specifically addressing the problem of obtaining prediction regions.
  arXiv  Detail & Related papers  (2024-11-26T02:19:13Z)
• Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering [55.15192437680943]
  Generative models lack rigorous statistical guarantees for their outputs. We propose a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee. This guarantee states that with high probability, the prediction sets contain at least one admissible (or valid) example.
  arXiv  Detail & Related papers  (2024-10-02T15:26:52Z)
• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• 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)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Sequential Predictive Conformal Inference for Time Series [16.38369532102931]
  We present a new distribution-free conformal prediction algorithm for sequential data (e.g., time series) We specifically account for the nature that time series data are non-exchangeable, and thus many existing conformal prediction algorithms are not applicable.
  arXiv  Detail & Related papers  (2022-12-07T05:07:27Z)
• Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction [0.0]
  split conformal prediction represents a promising avenue to obtain finite-sample guarantees under minimal distribution-free assumptions. We highlight the connection between split conformal prediction and classical tolerance predictors developed in the 1940s.
  arXiv  Detail & Related papers  (2022-10-26T14:12:24Z)
• Predictive Inference with Feature Conformal Prediction [80.77443423828315]
  We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
  arXiv  Detail & Related papers  (2022-10-01T02:57:37Z)
• A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting [0.0]
  This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) It enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable.
  arXiv  Detail & Related papers  (2022-07-28T16:40:26Z)
• Efficient and Differentiable Conformal Prediction with General Function Classes [96.74055810115456]
  We propose a generalization of conformal prediction to multiple learnable parameters. We show that it achieves approximate valid population coverage and near-optimal efficiency within class. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
  arXiv  Detail & Related papers  (2022-02-22T18:37:23Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

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.