Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regression

• URL: http://arxiv.org/abs/2503.02205v1
• Date: Tue, 04 Mar 2025 02:34:59 GMT
• Title: Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regression
• Authors: Rui Luo, Zhixin Zhou,
• Abstract summary: We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty in multi-target regression.<n>We show that VSPS produces smaller, more regions while maintaining informative prediction modeling in complex, high-dimensional settings.
• Score: 9.559062601251464
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal calibration. This approach constructs flexible, non-convex predictive regions with guaranteed coverage probabilities, overcoming limitations of traditional methods. By learning a transformation where the conditional distribution of responses follows a known form, VSPS identifies dense regions in the original space using the Jacobian determinant. This enables the creation of prediction regions that adapt to the true underlying distribution, focusing on areas of high probability density. Experimental results demonstrate that VSPS produces smaller, more informative prediction regions while maintaining robust coverage guarantees, enhancing uncertainty modeling in complex, high-dimensional settings.

Related papers

• Conformal Prediction Adaptive to Unknown Subpopulation Shifts [11.046912341345294]
  Conformal prediction is widely used to equip black-box machine learning models with uncertainty quantification enjoying formal coverage guarantees.<n>In this work, we address subpopulation shifts, where the test environment exhibits an unknown and differing mixture of subpopulations compared to the calibration data.<n>We propose new methods that provably adapt conformal prediction to such shifts, ensuring valid coverage without requiring explicit knowledge of subpopulation structure.
  arXiv  Detail & Related papers  (2025-06-05T20:58:39Z)
• JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows [7.200880964149064]
  Conformal prediction provides a model-agnostic framework for uncertainty quantification with finite-sample validity guarantees.<n>Existing approaches commonly rely on residual-based conformity scores, which impose geometric constraints.<n>We introduce JAPAN (Joint Adaptive Prediction Areas with Normalising-Flows), a conformal prediction framework that uses density-based conformity scores.
  arXiv  Detail & Related papers  (2025-05-29T07:34:51Z)
• Rectifying Conformity Scores for Better Conditional Coverage [75.73184036344908]
  We present a new method for generating confidence sets within the split conformal prediction framework.<n>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)
• Optimal Transport-based Conformal Prediction [8.302146576157497]
  Conformal Prediction (CP) is a principled framework for uncertainty in blackbox learning models.<n>We introduce a novel CP procedure handling prediction score functions through a lens.<n>We then adapt our method for quantifying multi-output regression and multiclass classification.
  arXiv  Detail & Related papers  (2025-01-31T09:48:28Z)
• 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)
• Generative Conformal Prediction with Vectorized Non-Conformity Scores [6.059745771017814]
  Conformal prediction provides model-agnostic uncertainty quantification with guaranteed coverage.<n>We propose a generative conformal prediction framework with vectorized non-conformity scores.<n>We construct adaptive uncertainty sets using density-ranked uncertainty balls.
  arXiv  Detail & Related papers  (2024-10-17T16:37:03Z)
• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors.<n>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)
• 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)
• Federated Conformal Predictors for Distributed Uncertainty Quantification [83.50609351513886]
  Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning. In this paper, we extend conformal prediction to the federated learning setting. We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction framework.
  arXiv  Detail & Related papers  (2023-05-27T19:57:27Z)
• Variational Inference with Coverage Guarantees in Simulation-Based Inference [18.818573945984873]
  We propose Conformalized Amortized Neural Variational Inference (CANVI) CANVI constructs conformalized predictors based on each candidate, compares the predictors using a metric known as predictive efficiency, and returns the most efficient predictor. We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.
  arXiv  Detail & Related papers  (2023-05-23T17:24:04Z)
• 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)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)

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.