Generalizes conformal prediction to multiple learnable parameters for efficient prediction sets.
problem Learning valid and efficient prediction sets with low-capacity function classes.
method Constrained empirical risk minimization (ERM) with gradient-based optimization of differentiable surrogate losses and Lagrangians.
result Achieves approximate valid population coverage and near-optimal efficiency within class.
New bounds on efficiency for conformalized regression methods.
problem Efficiency of conformal prediction in regression models.
method Non-asymptotic bounds on prediction set length for conformalized quantile and median regression.
result Identifies phase transitions in convergence rates across different regimes of miscoverage level.
This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.
problem Efficiently combining multiple conformity scores to reduce prediction set size.
method Introduces COnfidence-Level Allocation (COLA) to optimally allocate confidence levels across sets.
result COLA achieves smaller prediction sets than state-of-the-art methods while maintaining valid coverage.
PAC-Bayes theory improves ICP efficiency and coverage.
problem Inefficient and unreliable uncertainty estimates in deep learning models.
method PAC-Bayes theory to optimize model and score function parameters for efficient and reliable prediction sets.
result Generalization bounds on coverage and efficiency of optimized prediction sets.
The paper finds a fundamental trade-off between confidence and efficiency in transductive conformal prediction.
problem The challenge is to balance confidence and efficiency in predicting multiple data points.
method The authors derive a strict finite-sample bound and introduce a practical algorithm to approach this bound.
result Any non-trivial confidence level leads to exponential growth in prediction set size, with a linear scaling in the number of samples.
Proposes SCD-split for CP to balance interpretability and efficiency.
problem Difficult interpretation of disconnected subintervals in CP prediction sets.
method Incorporates smoothing operations into CP framework.
result SCD-split balances interval length and subinterval number, theoretically provable.
Paper introduces PCP for efficient, reliable predictive inference.
problem Developing reliable predictive inference methods for target variables.
method Probabilistic conformal prediction using conditional random samples.
result PCP provides sharper predictive sets compared to existing methods.
SACP aggregates nonconformity scores from multiple predictors to create more efficient uncertainty sets.
problem Combining predictive uncertainties from multiple models for efficient and reliable uncertainty quantification.
method SACP (Symmetric Aggregated Conformal Prediction) aggregates nonconformity scores using a flexible symmetric aggregation function.
result SACP consistently improves efficiency and often outperforms state-of-the-art model aggregation baselines.
CTI produces efficient prediction intervals with guaranteed coverage.
problem Efficient and reliable uncertainty quantification in regression.
method CTI estimates conditional density for interval length, then thresholds intervals based on this density.
result CTI achieves smaller prediction sets with guaranteed coverage compared to existing methods.
DANCE improves prediction set efficiency for deep learning models.
problem Inefficient, overly conservative prediction sets for pre-trained models.
method DANCE combines adaptive kernel regression and nearest-neighbor approach.
result DANCE produces more efficient and robust prediction sets.
Differentially private conformal prediction improves statistical efficiency.
problem Quantifying uncertainty in private data analysis.
method Introducing differential conformal prediction and developing Differentially Private Conformal Prediction (DPCP).
result DPCP produces tighter prediction sets than existing private split conformal approaches.
Root-finding methods improve efficiency of conformal prediction sets.
problem Efficiently computing conformal prediction sets is difficult.
method Exploiting root-finding algorithms to approximate boundaries of conformal prediction intervals.
result The approach can overcome limitations of previous strategies.
SPI uses synthetic data to improve predictive inference efficiency.
problem Inefficient predictive inference with scarce calibration data.
method Integrates synthetic data to align nonconformity scores and improve coverage guarantees.
result SPI yields substantially tighter and more informative prediction sets.
Advice-efficient prediction with expert advice (in analogy to label-efficient prediction) is a variant of prediction with expert advice game, where on each round of the game we are allowed to ask for advice of a limited number M out of N experts. This setting is especially interesting when asking for advice of ever…
EENNs improve inference efficiency but need nested prediction sets for reliable uncertainty estimates.
problem Non-nested prediction sets from standard uncertainty quantification methods in EENNs.
method Introduced anytime-valid confidence sequences (AVCSs) tailored for EENNs.
result AVCSs generate nested prediction sets across EENN exits, addressing the issue of non-nested sets.
Study robustness of split conformal prediction in data contamination setting.
problem Robustness of split conformal prediction under data contamination.
method Analyze split conformal prediction's performance in a contaminated data setting and propose a new method.
result Demonstrated the impact of corrupted data on prediction intervals' coverage and efficiency.
New methods improve cross-conformal prediction's prediction sets without sacrificing coverage guarantees.
problem Improving the width of prediction sets in cross-conformal prediction.
method Proposed new variants of existing methods based on recent results on more efficient combination of p-values.
result Smaller prediction sets achieved without compromising theoretical guarantees.
Efficient method predicts plausible probability ranges for credal sets.
problem Computational complexity in training credal predictors for complex models.
method Grounded in relative likelihood, decalibration technique.
result Yields credal sets with strong performance across diverse tasks.
Study improves treatment effect estimation using unlabeled covariates.
problem Estimating treatment effects with limited labeled data.
method Developed efficiency bounds and estimators for semi-supervised setting.
result Estimators using unlabeled covariates have lower asymptotic variance.
Framework uses dropout to efficiently explore Rashomon set for multiplicity estimation.
problem Efficiently measuring and mitigating conflicting model outputs in classification tasks.
method Dropout-based exploration of Rashomon set for multiplicity estimation.
result Framework outperforms baselines in multiplicity metric estimation with significant runtime speedup.
Efficiently combines autoregressive and set-based models for joint distributions.
problem Joint distributions over multiple predictions from set-based models.
method Causal autoregressive buffer that caches context and captures dependencies.
result Up to 20x faster joint sampling and density evaluation, up to 7x lower memory usage.
New method converts p-values to e-values for more efficient CP and aggregation.
problem Limitations of existing p-to-e calibrators in CP setting.
method Proposes a novel P2E calibrator for set-preserving calibration.
result Significant efficiency gains over existing p-to-e calibrators.
Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conf…
New algorithms reduce label collection for online prediction with expert advice.
problem Efficiently predicting binary sequences with expert advice using fewer labels.
method Adaptive selective sampling for exponentially weighted forecasters.
result Label complexity scales roughly as the square root of the number of rounds for a scenario with a strictly better expert.
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optim…
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
PPI++ uses machine learning predictions to improve inference from small datasets.
problem Efficient inference from small labeled datasets with high-quality predictions.
method Adapts prediction-powered inference (PPI) to compute confidence sets for any parameter dimensionality.
result Improves classical intervals using only labeled data, always yielding better results.
Bayesian GNNs with temperature improve prediction efficiency in CP.
problem Efficiency of prediction sets in CP for GNNs.
method Introducing a temperature parameter into Bayesian GNNs within the CP framework.
result More efficient prediction sets achieved with the temperature parameter.
A new method improves efficiency of conformal prediction for ensemble models.
problem Efficiently estimating uncertainty for ensemble models without distributional assumptions.
method Proposes a multivariate score function to merge prediction regions of individual models, reducing conservatism.
result Demonstrates more efficient prediction regions compared to existing methods.
Efficiently samples conformal boundaries in high dimensions using flows.
problem Difficulty in interpreting and using prediction sets in high-dimensional or structured output spaces.
method Flow-based approach using differentiable nonconformity scores to induce deterministic flows on the output space.
result Sampling conformal boundaries in arbitrary dimensions becomes computationally efficient and training-free.
The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.
problem Constructing well-calibrated prediction sets for correlated target variables.
method The method uses vine copulas to estimate the joint cumulative distribution function of non-conformity scores and improves the asymptotic efficiency of the quantile estimate.
result The method guarantees asymptotically exact coverage and competitive efficiency on real-world regression problems.
A new approach reduces the size of prediction sets in CP, making it more efficient.
problem Large and costly prediction sets in standard CP.
method Expand correctness criterion to include inferred admissible answers, and use prediction cascades to prune implausible labels.
result Empirically effective in natural language processing and drug discovery.
coverforest speeds up conformal predictions for random forests.
problem Efficient uncertainty quantification for random forest predictions.
method Optimized Python package leveraging random forest's out-of-bag scores for cross-conformal predictions.
result coverforest achieves desired coverage with faster training and prediction times.
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the ca…
Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions (often considerably more restrictive) of the underlying algorithm ar…
New method for robust prediction valid under non-exchangeable data.
problem Non-exchangeability in data violates robustness of common CP methods.
method Introduces efficient CP approach for general non-exchangeable data.
result Produces provably valid confidence sets for non-exchangeable data.
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust model parameters automatically based on the data, but often require users to set additional parameters, known as hyperparameters. Hyperparamete…
GC-FCP provides efficient federated CP with group-conditional coverage guarantees.
problem Uncertainty quantification in federated settings with distributed calibration data.
method Group-conditional federated conformal prediction (GC-FCP) using group-stratified coresets.
result GC-FCP offers efficient aggregation and calibration compared to centralized methods.
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
problem Need for uncertainty quantification with multiple confidence levels in diverse applications.
method Online optimization perspective to enforce nestedness of prediction sets while controlling quantile estimation error.
result Achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency.
Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.
problem Improving the reliability and uncertainty quantification of machine learning predictions.
method Combines Venn-Abers calibration and conformal prediction for binary and regression problems.
result Improves interval efficiency through model calibration and offers practical alternatives.
The paper introduces uncertainty quantification for NER models.
problem Current NER models lack uncertainty measures, leading to downstream errors.
method Full-Sequence and Subsequence Conformal Prediction framework.
result The method provides formal guarantees about the reliability of model predictions.
Study efficient algorithms for one-shot federated conformal prediction.
problem Valid prediction sets in one-shot federated learning.
method Quantile-of-quantiles family of estimators and split conformal prediction.
result No significant loss in coverage and length compared to centralized setting.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
Develops CPL for optimal prediction set length and validity.
problem Balancing conditional validity and length efficiency in conformal prediction.
method Conformal Prediction with Length-Optimization (CPL).
result Achieves optimal prediction set length while maintaining conditional validity.
NCP improves deep classifier uncertainty quantification efficiency.
problem Uncertainty quantification for deep classifiers in high-stake applications.
method Neighborhood Conformal Prediction (NCP) algorithm.
result NCP produces smaller prediction sets than traditional CP methods.
New algorithms for hierarchical classification using conformal prediction.
problem Valid prediction sets in hierarchical classification tasks.
method Extended split conformal prediction framework with two inference algorithms.
result Empirical evaluations show effectiveness in achieving nominal coverage.
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
problem Helping experts make accurate decisions in multiclass classification tasks.
method Develops an automated decision support system using conformal prediction to provide precise prediction sets and an efficient search method.
result System improves expert predictions by providing precise prediction sets and forcing experts to predict from these sets.
Develops efficient time series prediction intervals.
problem Constructing reliable prediction intervals for time series data.
method Introduces exttt{EnbPI} algorithm for time series data.
result Demonstrates superior performance compared to existing methods.