Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization

• URL: http://arxiv.org/abs/2512.15383v1
• Date: Wed, 17 Dec 2025 12:36:00 GMT
• Title: Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization
• Authors: Yunjie Fan, Matteo Sesia,
• Abstract summary: We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs.<n>This method can be combined with any multi-target regression model and guarantees finite-sample coverage.<n>The core idea is a novel emphcoordinate-wise standardization procedure that makes residuals across output dimensions directly comparable.
• Score: 11.505405148959618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs given a single input. This method can be combined with any multi-target regression model and guarantees finite-sample coverage. It is computationally efficient and yields informative prediction intervals even with limited data. The core idea is a novel \emph{coordinate-wise} standardization procedure that makes residuals across output dimensions directly comparable, estimating suitable scaling parameters using the calibration data themselves. This does not require modeling of cross-output dependence nor auxiliary sample splitting. Implementing this idea requires overcoming technical challenges associated with transductive or full conformal prediction. Experiments on simulated and real data demonstrate this method can produce tighter prediction intervals than existing baselines while maintaining valid simultaneous coverage.

Related papers

• Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching [60.37045080890305]
  We provide the first analysis of the sample complexity for flow-matching based generative models.<n>We decompose the velocity field estimation error into neural-network approximation error, statistical error due to the finite sample size, and optimization error due to the finite number of optimization steps for estimating the velocity field.
  arXiv  Detail & Related papers  (2025-12-01T05:14:25Z)
• ResCP: Reservoir Conformal Prediction for Time Series Forecasting [39.81023599249223]
  Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data.<n>We propose Reservoir Conformal Prediction (ResCP), a novel training-free conformal prediction method for time series.
  arXiv  Detail & Related papers  (2025-10-06T17:37:44Z)
• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We address the problem of uncertainty quantification in time series by exploiting correlated sequences.<n>We propose a novel distribution-free approach based on conformal prediction framework and quantile regression.<n>Our approach provides accurate coverage and achieves state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering [55.15192437680943]
  Generative models lack rigorous statistical guarantees for their outputs.<n>We propose a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee.<n>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)
• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
  Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs.<n>We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint.<n>We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
  arXiv  Detail & Related papers  (2024-06-05T13:36:38Z)
• Conditional validity of heteroskedastic conformal regression [12.905195278168506]
  Conformal prediction and split conformal prediction offer a distribution-free approach to estimating prediction intervals with statistical guarantees. Recent work has shown that split conformal prediction can produce state-of-the-art prediction intervals when focusing on marginal coverage. This paper tries to shed new light on how prediction intervals can be constructed, using methods such as normalized and Mondrian conformal prediction.
  arXiv  Detail & Related papers  (2023-09-15T11:10:46Z)
• Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing [6.288767115532775]
  Fog manufacturing can greatly enhance traditional manufacturing systems through distributed computation Fog units. It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification of runtime performance metrics. We propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics.
  arXiv  Detail & Related papers  (2022-10-10T16:14:08Z)
• 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)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Multivariate Probabilistic Regression with Natural Gradient Boosting [63.58097881421937]
  We propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
  arXiv  Detail & Related papers  (2021-06-07T17:44:49Z)
• Conformal histogram regression [15.153110906331737]
  This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while achieving conditional coverage and optimal length if the black-box model is consistent.
  arXiv  Detail & Related papers  (2021-05-18T18:05:02Z)

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.