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Tag: prediction

  • Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions

    Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions arXiv:2603.04635v1 Announce Type: new Abstract: Independence testing is a fundamental problem in statistical inference: given samples from a joint distribution $p$ over multiple random variables, the goal is to determine whether $p$ is a product distribution or is $epsilon$-far from all product distributions in total variation distance.…

  • Conformal Graph Prediction with Z-Gromov Wasserstein Distances

    Conformal Graph Prediction with Z-Gromov Wasserstein Distances arXiv:2603.02460v1 Announce Type: new Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph–valued prediction, principled uncertainty quantification remains limited. We propose a conformal prediction framework for graph-valued outputs, providing distribution–free coverage guarantees in structured output spaces. Our method…

  • Dissecting Performative Prediction: A Comprehensive Survey

    Dissecting Performative Prediction: A Comprehensive Survey arXiv:2602.10176v1 Announce Type: new Abstract: The field of performative prediction had its beginnings in 2020 with the seminal paper “Performative Prediction” by Perdomo et al., which established a novel machine learning setup where the deployment of a predictive model causes a distribution shift in the environment, which in turn…

  • 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…

  • Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction

    Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction arXiv:2601.05297v1 Announce Type: new Abstract: Accurate prediction of structural dynamics is imperative for preserving digital twin fidelity throughout operational lifetimes. Parametric models with fixed nominal parameters often omit critical physical effects due to simplifications in geometry, material behavior, damping, or boundary conditions,…

  • 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…

  • Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms

    Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms arXiv:2512.14221v1 Announce Type: new Abstract: Conformal prediction (CP) offers a principled framework for uncertainty quantification, but it fails to guarantee coverage when faced with missing covariates. In addressing the heterogeneity induced by various missing patterns, Mask-Conditional Valid (MCV) Coverage has emerged…

  • 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…

  • Effects of label noise on the classification of outlier observations

    Effects of label noise on the classification of outlier observations arXiv:2511.08808v1 Announce Type: new Abstract: This study investigates the impact of adding noise to the training set classes in classification tasks using the BCOPS algorithm (Balanced and Conformal Optimized Prediction Sets), proposed by Guan & Tibshirani (2022). The BCOPS algorithm is an application of conformal…

  • 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…

  • Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

    Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis arXiv:2510.23935v1 Announce Type: new Abstract: Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we…

  • 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…

  • Prediction vs. Search Models: What Data Scientists Are Missing

    Prediction vs. Search Models: What Data Scientists Are Missing How do platform firms set prices and make money? The post Prediction vs. Search Models: What Data Scientists Are Missing appeared first on Towards Data Science. Derek Tran Go to original source

  • Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction

    Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction arXiv:2509.04631v1 Announce Type: cross Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is to construct a prediction set that includes the true outcomes with the prescribed confidence. We demonstrate a fundamental trade-off between confidence and…

  • Prediction-Powered Inference with Inverse Probability Weighting

    Prediction-Powered Inference with Inverse Probability Weighting arXiv:2508.10149v1 Announce Type: new Abstract: Prediction-powered inference (PPI) is a recent framework for valid statistical inference with partially labeled data, combining model-based predictions on a large unlabeled set with bias correction from a smaller labeled subset. We show that PPI can be extended to handle informative labeling by replacing…

  • PAC Off-Policy Prediction of Contextual Bandits

    PAC Off-Policy Prediction of Contextual Bandits arXiv:2507.16236v1 Announce Type: new Abstract: This paper investigates off-policy evaluation in contextual bandits, aiming to quantify the performance of a target policy using data collected under a different and potentially unknown behavior policy. Recently, methods based on conformal prediction have been developed to construct reliable prediction intervals that guarantee…

  • Conformalized Regression for Continuous Bounded Outcomes

    Conformalized Regression for Continuous Bounded Outcomes arXiv:2507.14023v1 Announce Type: new Abstract: Regression problems with bounded continuous outcomes frequently arise in real-world statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting a response associated with a new covariate value. Most of the existing statistical…

  • 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…

  • Conformal Prediction for Long-Tailed Classification

    Conformal Prediction for Long-Tailed Classification arXiv:2507.06867v1 Announce Type: new Abstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii)…

  • Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction

    Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction arXiv:2506.21802v1 Announce Type: new Abstract: Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue…

  • 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…

  • Collaborative Prediction: To Join or To Disjoin Datasets

    Collaborative Prediction: To Join or To Disjoin Datasets arXiv:2506.11271v1 Announce Type: new Abstract: With the recent rise of generative Artificial Intelligence (AI), the need of selecting high-quality dataset to improve machine learning models has garnered increasing attention. However, some part of this topic remains underexplored, even for simple prediction models. In this work, we study…

  • Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces

    Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces arXiv:2506.08325v1 Announce Type: new Abstract: Depth measures are powerful tools for defining level sets in emerging, non–standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of…

  • JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows

    JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows arXiv:2505.23196v1 Announce Type: new Abstract: Conformal prediction provides a model-agnostic framework for uncertainty quantification with finite-sample validity guarantees, making it an attractive tool for constructing reliable prediction sets. However, existing approaches commonly rely on residual-based conformity scores, which impose geometric constraints and struggle when the underlying distribution is…

  • 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,…

  • Minimum Volume Conformal Sets for Multivariate Regression

    Minimum Volume Conformal Sets for Multivariate Regression arXiv:2503.19068v1 Announce Type: new Abstract: Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables, existing multivariate methods either impose rigid geometric assumptions or rely on flexible but computationally expensive approaches that do not…

  • 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 L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

    Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations arXiv:2502.14105v1 Announce Type: new Abstract: Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using L’evy-Prokhorov (LP) ambiguity sets, which…

  • Prediction-Powered Adaptive Shrinkage Estimation

    Prediction-Powered Adaptive Shrinkage Estimation arXiv:2502.14166v1 Announce Type: new Abstract: Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While prior work has demonstrated PPI’s benefits for individual statistical tasks, modern applications require answering numerous parallel statistical questions. We introduce Prediction-Powered Adaptive Shrinkage (PAS),…

  • Generative Distribution Prediction: A Unified Approach to Multimodal Learning

    Generative Distribution Prediction: A Unified Approach to Multimodal Learning arXiv:2502.07090v1 Announce Type: new Abstract: Accurate prediction with multimodal data-encompassing tabular, textual, and visual inputs or outputs-is fundamental to advancing analytics in diverse application domains. Traditional approaches often struggle to integrate heterogeneous data types while maintaining high predictive accuracy. We introduce Generative Distribution Prediction (GDP), a…

  • Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction

    Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction arXiv:2502.05676v1 Announce Type: new Abstract: Ensuring model calibration is critical for reliable predictions, yet popular distribution-free methods, such as histogram binning and isotonic regression, provide only asymptotic guarantees. We introduce a unified framework for Venn and Venn-Abers calibration, generalizing Vovk’s binary classification approach to arbitrary…

  • 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…

  • Optimal Transport-based Conformal Prediction

    Optimal Transport-based Conformal Prediction arXiv:2501.18991v1 Announce Type: new Abstract: Conformal Prediction (CP) is a principled framework for quantifying uncertainty in blackbox learning models, by constructing prediction sets with finite-sample coverage guarantees. Traditional approaches rely on scalar nonconformity scores, which fail to fully exploit the geometric structure of multivariate outputs, such as in multi-output regression or…

  • 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…

  • Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score

    Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score How confident should I be in a machine learning model’s prediction for a new data point? Could I get a range of likely values? Image by author When working on a supervised task, machine learning models can be used to predict the outcome for…

  • Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate

    Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate arXiv:2412.11257v1 Announce Type: new Abstract: Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though this…

  • GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction

    GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction arXiv:2412.08661v1 Announce Type: new Abstract: Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally…

  • Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series

    Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series arXiv:2411.17042v1 Announce Type: new Abstract: Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity guarantees. While easily adaptable to non-probabilistic models, applying conformal prediction to probabilistic generative models, such as Normalising…

  • Spatio-Temporal Conformal Prediction for Power Outage Data

    Spatio-Temporal Conformal Prediction for Power Outage Data arXiv:2411.17099v1 Announce Type: new Abstract: In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the power industry. To enable rapid recovery, accurately predicting future…