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Tag: conformal

  • LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

    LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees arXiv:2602.22432v1 Announce Type: new Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage, yet split conformal uses a single global residual quantile and can be poorly adaptive under…

  • Flow-Based Conformal Predictive Distributions

    Flow-Based Conformal Predictive Distributions arXiv:2602.07633v1 Announce Type: new Abstract: Conformal prediction provides a distribution-free framework for uncertainty quantification via prediction sets with exact finite-sample coverage. In low dimensions these sets are easy to interpret, but in high-dimensional or structured output spaces they are difficult to represent and use, which can limit their ability to integrate…

  • Time-uniform conformal and PAC prediction

    Time-uniform conformal and PAC prediction arXiv:2602.06297v1 Announce Type: new Abstract: Given that machine learning algorithms are increasingly being deployed to aid in high stakes decision-making, uncertainty quantification methods that wrap around these black box models such as conformal prediction have received much attention in recent years. In sequential settings, where data are observed/generated in a…

  • Approximate full conformal prediction in RKHS

    Approximate full conformal prediction in RKHS arXiv:2601.13102v1 Announce Type: new Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estimators. However, a classical limitation of the full conformal framework is the computation of the confidence prediction regions, which is usually impossible since it requires training…

  • CAOS: Conformal Aggregation of One-Shot Predictors

    CAOS: Conformal Aggregation of One-Shot Predictors arXiv:2601.05219v1 Announce Type: new Abstract: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and…

  • Fast Conformal Prediction using Conditional Interquantile Intervals

    Fast Conformal Prediction using Conditional Interquantile Intervals arXiv:2601.02769v1 Announce Type: new Abstract: We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional…

  • Conformal Blindness: A Note on $A$-Cryptic change-points

    Conformal Blindness: A Note on $A$-Cryptic change-points arXiv:2601.01147v1 Announce Type: new Abstract: Conformal Test Martingales (CTMs) are a standard method within the Conformal Prediction framework for testing the crucial assumption of data exchangeability by monitoring deviations from uniformity in the p-value sequence. Although exchangeability implies uniform p-values, the converse does not hold. This raises the…

  • Conformal Prediction for Compositional Data

    Conformal Prediction for Compositional Data arXiv:2511.18141v1 Announce Type: new Abstract: In this work, we propose a set of conformal prediction procedures tailored to compositional responses, where outcomes are proportions that must be positive and sum to one. Building on Dirichlet regression, we introduce a split conformal approach based on quantile residuals and a highest-density region…

  • Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

    Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands arXiv:2511.13911v1 Announce Type: new Abstract: Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their clinical deployment. To enable safe and reliable use of such predictions in healthcare, we introduce a conformal method…

  • Conformal Prediction Beyond the Horizon: Distribution-Free Inference for Policy Evaluation

    Conformal Prediction Beyond the Horizon: Distribution-Free Inference for Policy Evaluation arXiv:2510.26026v1 Announce Type: new Abstract: Reliable uncertainty quantification is crucial for reinforcement learning (RL) in high-stakes settings. We propose a unified conformal prediction framework for infinite-horizon policy evaluation that constructs distribution-free prediction intervals {for returns} in both on-policy and off-policy settings. Our method integrates distributional…

  • Conformal Inference for Open-Set and Imbalanced Classification

    Conformal Inference for Open-Set and Imbalanced Classification arXiv:2510.13037v1 Announce Type: new Abstract: This paper presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represented in the data. Existing approaches require a finite, known label space and typically involve random sample…

  • On some practical challenges of conformal prediction

    On some practical challenges of conformal prediction arXiv:2510.10324v1 Announce Type: new Abstract: Conformal prediction is a model-free machine learning method for creating prediction regions with a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity…

  • Domain-Shift-Aware Conformal Prediction for Large Language Models

    Domain-Shift-Aware Conformal Prediction for Large Language Models arXiv:2510.05566v1 Announce Type: new Abstract: Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under…

  • Predictive inference for time series: why is split conformal effective despite temporal dependence?

    Predictive inference for time series: why is split conformal effective despite temporal dependence? arXiv:2510.02471v1 Announce Type: new Abstract: We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction intervals around our forecasts? To avoid placing distributional…

  • Neural Optimal Transport Meets Multivariate Conformal Prediction

    Neural Optimal Transport Meets Multivariate Conformal Prediction arXiv:2509.25444v1 Announce Type: new Abstract: We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal prediction. Classical quantile regression does not extend naturally to multivariate responses, while existing approaches often ignore the geometry of…

  • Online Conformal Selection with Accept-to-Reject Changes

    Online Conformal Selection with Accept-to-Reject Changes arXiv:2508.13838v1 Announce Type: new Abstract: Selecting a subset of promising candidates from a large pool is crucial across various scientific and real-world applications. Conformal selection offers a distribution-free and model-agnostic framework for candidate selection with uncertainty quantification. While effective in offline settings, its application to online scenarios, where data…

  • Conformal Data Contamination Tests for Trading or Sharing of Data

    Conformal Data Contamination Tests for Trading or Sharing of Data arXiv:2507.13835v1 Announce Type: new Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing with external data agents. However, data buyers need…

  • Class conditional conformal prediction for multiple inputs by p-value aggregation

    Class conditional conformal prediction for multiple inputs by p-value aggregation arXiv:2507.07150v1 Announce Type: new Abstract: Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an innovative refinement to these methods for classification tasks, specifically tailored for scenarios where multiple observations (multi-inputs) of a…

  • Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting

    Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting arXiv:2507.05470v1 Announce Type: new Abstract: We propose Temporal Conformal Prediction (TCP), a novel framework for constructing prediction intervals in financial time-series with guaranteed finite-sample validity. TCP integrates quantile regression with a conformal calibration layer that adapts online via a decaying…

  • Valid Selection among Conformal Sets

    Valid Selection among Conformal Sets arXiv:2506.20173v1 Announce Type: new Abstract: Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To…

  • Conformal Object Detection by Sequential Risk Control

    Conformal Object Detection by Sequential Risk Control arXiv:2505.24038v1 Announce Type: new Abstract: Recent advances in object detectors have led to their adoption for industrial uses. However, their deployment in critical applications is hindered by the inherent lack of reliability of neural networks and the complex structure of object detection models. To address these challenges, we…

  • Backward Conformal Prediction

    Backward Conformal Prediction arXiv:2505.13732v1 Announce Type: new Abstract: We introduce $textit{Backward Conformal Prediction}$, a method that guarantees conformal coverage while providing flexible control over the size of prediction sets. Unlike standard conformal prediction, which fixes the coverage level and allows the conformal set size to vary, our approach defines a rule that constrains how prediction…

  • Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach

    Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach arXiv:2505.04986v1 Announce Type: new Abstract: Conformal prediction is a powerful tool for constructing prediction intervals for black-box models, providing a finite sample coverage guarantee for exchangeable data. However, this exchangeability is compromised when some entries of the test feature are contaminated, such as in the case of…

  • From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning

    From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning arXiv:2504.15722v1 Announce Type: new Abstract: Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference time. However, uncertainty quantification for ICL remains an open challenge,…

  • Topology-Aware Conformal Prediction for Stream Networks

    Topology-Aware Conformal Prediction for Stream Networks arXiv:2503.04981v1 Announce Type: new Abstract: Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet challenging task. Traditional conformal prediction methods struggle in this setting due to the need for joint predictions across multiple interdependent locations and…

  • Conformal Prediction with Upper and Lower Bound Models

    Conformal Prediction with Upper and Lower Bound Models arXiv:2503.04071v1 Announce Type: new Abstract: This paper studies a Conformal Prediction (CP) methodology for building prediction intervals in a regression setting, given only deterministic lower and upper bounds on the target variable. It proposes a new CP mechanism (CPUL) that goes beyond post-processing by adopting a model…

  • Conformal Prediction Under Generalized Covariate Shift with Posterior Drift

    Conformal Prediction Under Generalized Covariate Shift with Posterior Drift arXiv:2502.17744v1 Announce Type: new Abstract: In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance…

  • On Volume Minimization in Conformal Regression

    On Volume Minimization in Conformal Regression arXiv:2502.09985v1 Announce Type: new Abstract: We study the question of volume optimality in split conformal regression, a topic still poorly understood in comparison to coverage control. Using the fact that the calibration step can be seen as an empirical volume minimization problem, we first derive a finite-sample upper-bound on…

  • Epistemic Uncertainty in Conformal Scores: A Unified Approach

    Epistemic Uncertainty in Conformal Scores: A Unified Approach arXiv:2502.06995v1 Announce Type: new Abstract: Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although recent conformal scores have been developed to address this limitation, they are typically designed for specific…

  • Multivariate Conformal Prediction using Optimal Transport

    Multivariate Conformal Prediction using Optimal Transport arXiv:2502.03609v1 Announce Type: new Abstract: Conformal prediction (CP) quantifies the uncertainty of machine learning models by constructing sets of plausible outputs. These sets are constructed by leveraging a so-called conformity score, a quantity computed using the input point of interest, a prediction model, and past observations. CP sets are…

  • Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates

    Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates arXiv:2501.14933v1 Announce Type: new Abstract: In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and public policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal…

  • coverforest: Conformal Predictions with Random Forest in Python

    coverforest: Conformal Predictions with Random Forest in Python arXiv:2501.14570v1 Announce Type: new Abstract: Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial…

  • Multi-Output Conformal Regression: A Unified Comparative Study with New Conformity Scores

    Multi-Output Conformal Regression: A Unified Comparative Study with New Conformity Scores arXiv:2501.10533v1 Announce Type: new Abstract: Quantifying uncertainty in multivariate regression is essential in many real-world applications, yet existing methods for constructing prediction regions often face limitations such as the inability to capture complex dependencies, lack of coverage guarantees, or high computational cost. Conformal prediction…

  • Adaptive Conformal Inference by Betting

    Adaptive Conformal Inference by Betting arXiv:2412.19318v1 Announce Type: new Abstract: Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference…