Paper introduces methods for more reliable probabilistic predictions with confidence intervals.
problem Inaccurate labeling of datasets due to unreliable probabilistic predictions from weak labeling functions.
method Proposes a methodology to provide confidence intervals for label probabilities using uncertainty sets of distributions.
result Improves reliability of probabilistic predictions and provides confidence intervals for label probabilities.
Exact PA algorithms learn to rank with interval labels.
problem Learning to rank with interval labels.
method Exact passive-aggressive algorithms solving convex optimization problems.
result Maintains threshold ordering and achieves accurate classifiers.
StratPPI improves prediction-powered inference with stratified sampling.
problem Improving statistical estimates with limited human-labeled data.
method Combining small human-labeled data with large automatic-labeled data, stratifying data for tighter confidence intervals.
result StratPPI provides substantially tighter confidence intervals than unstratified approaches.
Paper develops new conformal prediction methods for sum or average of unknown labels.
problem Uncertainty quantification in joint distributions of random variables.
method Introduces novel conformal prediction methods for sum or average of unknown labels.
result Validates the proposed method for sum or average of unknown labels under permutation invariant assumptions.
Multiple conventions have been adopted for denoting Interval Exchange Transformations (IETs). The "non-labeled" convention was the original, while the "labeled" convention has proven convenient when investigating Flat Surfaces as described by IETs. We establish the relationship between Extended Rauzy Classes, an equiva…
Active inference uses machine learning to prioritize data labeling for more efficient statistical inference.
problem Efficiently collecting data points for statistical inference with limited labels.
method A machine learning-assisted approach that identifies uncertain data points for labeling.
result Achieves the same level of accuracy with fewer samples, resulting in smaller confidence intervals and more powerful p-values.
Algorithm constructs prediction sets with PAC guarantees in label shift settings.
problem Reliable uncertainty quantification in the face of distribution shift.
method Estimates predicted probabilities and confusion matrix, then propagates uncertainty through Gaussian elimination to compute confidence intervals and construct prediction sets.
result Satisfies PAC guarantees and produces smaller, more informative prediction sets.
PPI uses proxy data to improve inference from limited labels across related tasks.
problem Statistical inference with limited labels across multiple related tasks.
method Prediction-powered inference framework that uses cross-task recalibration to improve power and accuracy.
result Cross-task recalibration can substantially reduce confidence interval widths when labels are scarce.
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.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
problem Accurate measurement of content violations that are often rare and costly to label.
method Design-based measurement system using ML-assisted probability sampling and LLM labeling.
result Produces unbiased prevalence estimates with confidence intervals and dashboard drilldowns.
PPAT uses predictions to improve risk estimation in active testing.
problem Exploiting informative predictions from black-box models for efficient risk estimation.
method Combines LURE estimator with prediction-powered control variate.
result PPAT outperforms existing methods in risk estimation and uncertainty quantification.
Two-step conformal prediction method for adaptive bounding box uncertainties in multi-object detection.
problem Quantifying predictive uncertainty for multi-object detection in safety-critical applications.
method Developed a two-step conformal prediction approach to propagate uncertainty in predicted class labels into bounding box uncertainties, ensuring coverage for incorrectly classified objects.
result Desired coverage levels are satisfied with practically tight predictive uncertainty intervals on real-world datasets.
Extends Fisher's Discriminant Analysis for interval-valued data.
problem Classifying entities represented by intervals and histograms.
method Adapts Fisher's Discriminant Analysis using Moore's interval arithmetic and Mallows' distance.
result Discriminant directions for interval-valued data are numerically maximized.
New methods for ordinal classification of interval-valued data and functional data.
problem Ordinal classification of interval-valued data and functional data.
method Six ordinal classifiers are proposed, including parametric, binary decomposition, logistic regression, distance-based, k-nearest-neighbor, kernel PCA, and random forest methods.
result Considering ordering and interval-valued information improves the accuracy of ordinal classification.
Deep ReLU networks generalize via piecewise linear interpolation.
problem Understanding how deep neural networks generalize well on real datasets despite memorizing training samples.
method Demonstrated that deep ReLU networks generalize via piecewise linear interpolation, providing a quantified analysis of generalization intervals.
result Generalization intervals of ReLU networks behave similarly along pairwise directions between samples of the same label in both real and random cases, suggesting the same interpolation mechanism.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
problem Statistical inference challenges with missing at random labels and spatial dependence.
method Doubly robust estimator with cross-fit nuisances and jackknife spatial HAC variance correction.
result Asymptotically valid confidence intervals with improved finite-sample calibration.
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…
Paper introduces RPWithPrior for efficient label differential privacy in regression.
problem Protecting user privacy in regression tasks with minimal accuracy loss.
method Modeling responses as continuous random variables, avoiding discretization; estimating optimal intervals for randomized responses.
result RPWithPrior algorithm guarantees ε-label differential privacy and outperforms existing methods.
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are…
Accelerates DNN robustness verification with target labels.
problem Improving the robustness of deep neural networks against adversarial attacks.
method Guiding robustness verification with target labels, reducing search space and using symbolic interval propagation and linear relaxation.
result Significantly improves DNN verification speed by 36X, especially when perturbation distance is reasonable.
This paper tackles uncertainty in deep learning for construction of prediction intervals.
problem Deep learning models lack the ability to provide reliable prediction intervals for high-risk tasks.
method The authors design a special loss function to learn both aleatory and epistemic uncertainties without requiring uncertainty labels.
result The method constructs prediction intervals that are competitive with state-of-the-art methods on publicly available datasets.
MAPS algorithm creates reliable prediction intervals for high-dimensional data.
problem Computing reliable conditional prediction intervals in high-dimensional settings.
method Lifted predictive model (LPM) and MAPS algorithm for distribution-free intervals.
result MAPS algorithm produces valid prediction intervals for any trained model.
Ongoing developments in neural network models are continually advancing the state of the art in terms of system accuracy. However, the predicted labels should not be regarded as the only core output; also important is a well-calibrated estimate of the prediction uncertainty. Such estimates and their calibration are cri…
Proposes methods to aggregate prediction intervals for domain shift uncertainty.
problem Uncertainty quantification in distribution shifts.
method Aggregates prediction intervals for minimal width and adequate coverage.
result Effective methodologies for unsupervised domain shift under labeled source and unlabeled target.
Normalizing Flows improve prediction interval efficiency in CP.
problem Inefficient prediction intervals in CP due to non-uniform error distribution.
method Train a Normalizing Flow to optimize the distance metric between errors and inputs.
result Optimized prediction intervals are more efficient and valid.
The paper introduces moment multicalibration for estimating uncertainty across subgroups.
problem Ensuring fairness and accurate uncertainty estimation in predictions across different subgroups.
method Develops a method for multicalibration of higher moments, enabling point predictions and interval estimation.
result Moment multicalibration allows for valid prediction intervals that are fair across various subgroups.
Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.
problem Bias in LLM evaluations due to imperfect sensitivity and specificity.
method Plug-in framework with confidence intervals accounting for test and calibration dataset uncertainties.
result LML-based evaluation yields more reliable estimates than human-only evaluation.
This work approximates full conformal prediction for neural networks without sample splitting.
problem Uncertainty quantification for neural network regression models.
method Approximating full conformal prediction using Gauss-Newton influence for post-hoc uncertainty estimation.
result Locally-adaptive and often tighter prediction intervals compared to split-CP.
In this paper, we compare two definitions of Rauzy classes. The first one was introduced by Rauzy and was in particular used by Veech to prove the ergodicity of the Teichmüller flow. The second one is more recent and uses a "labeling" of the underlying intervals, and was used in the proof of some recent major results a…
AI-generated variables bias regression estimates; methods correct for invalid inference.
problem Bias in regression estimates due to AI-generated variables.
method Two methods: bias correction and joint estimation.
result Valid inference restored through proposed methods.
A new learning rule consistently reduces error over data samples.
problem Finding a learning rule that consistently reduces error over all data distributions.
method A deterministic, data-dependent partitioning rule that only partitions cyclic intervals with sufficient empirical diversity of labels.
result The expected error is monotone non-increasing with the sample size under every data distribution.
PPI uses predictions to improve inference from incomplete data.
problem Incomplete or costly-to-measure outcomes in research fields.
method Leverages large unlabeled datasets for improved statistical efficiency with bias correction.
result PPI variants produce tighter confidence intervals than complete-case analysis.
OPAL optimizes labeling strategy for precise inference from uncertain models.
problem Inference from uncertain machine learning models is brittle.
method OPAL learns a smooth policy to adaptively label data points based on model uncertainty.
result OPAL yields estimators with the lowest variance and achieves nominal coverage in finite samples.
Extends PPI to sequential setting, improving inference over time.
problem Sequential data growth with unlabelled data.
method Prediction-powered confidence sequence procedures using Ville's inequality and the method of mixtures.
result Asymptotically valid uniformly over time, accommodating prior knowledge.
Proposes a new network for accurate predictions and uncertainty estimation.
problem Uncertainty estimation in regression predictions without sacrificing accuracy.
method Decoupled two-stage training process with custom loss function.
result Reduces prediction error by 23-34% while maintaining 95% PICP.
New sampling methods improve classifier performance estimation.
problem Efficiently selecting data points to estimate classifier performance.
method Introduced and compared Importance Sampling and Poisson Sampling.
result Poisson Sampling outperforms Importance Sampling.
The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the la…
We present a class of knots associated with labelled generic immersions of intervals into the plane and compute their Gordian numbers and 4-dimensional invariants. At least 10% of the knots in Rolfsen's table belong to this class of knots. We call them track knots. They are contained in the class of quasipositive knots…
CF-GNN provides reliable uncertainty estimates for graph data.
problem Lack of rigorous uncertainty estimates in GNNs.
method Conformal prediction extended to graph-based models (CF-GNN).
result CF-GNN produces prediction sets with provable coverage probability.
New method for estimating class proportions in open-set label shift data.
problem Estimating class proportions and distributions when test data includes novel classes.
method Semiparametric density ratio model framework with maximum empirical likelihood estimators and confidence intervals.
result Improved estimation accuracy and classification performance compared to existing methods.
Efficiently evaluate generative models at the prompt level using tensor factorization.
problem Fine-grained evaluations of generative models are costly and often misaligned with human judgment.
method Tensor factorization model that merges cheap autorater data with a small set of human gold-standard labels.
result The method provides accurate and tight confidence intervals for model performance.
Cross-prediction improves inference from small labeled datasets.
problem Valid inference from small labeled datasets with imperfect predictions.
method Imputes missing labels via machine learning and debiases predictions.
result Inferences achieve desired error probability and are more powerful.
Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.
problem Online learning with set-valued feedback, where labels are sets rather than single labels.
method Introduced new combinatorial dimensions (Set Littlestone and Measure Shattering) to characterize learnability.
result Characterized deterministic and randomized online learnability, and established bounds for various learning settings.
Improves deep learning robustness by considering task and model.
problem Adversarial attacks on deep learning systems.
method Binary and interval label encoding strategy to redefine classification tasks and design corresponding loss functions.
result Our method enhances robustness without sacrificing accuracy.
CAP algorithm controls FCR in online selective prediction.
problem Online predictive tasks with temporal multiplicity and FCR control.
method CAP framework with adaptive pick rule and calibration set construction.
result CAP achieves exact selection-conditional coverage guarantee and FCR control.
Generative Augmented Inference improves AI-generated data for causal inference.
problem Challenges in using AI-generated annotations for reliable causal inference.
method Generative Augmented Inference (GAI) treats AI outputs as informative features for learning true labels, flexibly modeling the relationship using nonparametric methods.
result GAI significantly reduces estimation error and improves confidence interval quality compared to human-only and PPI-based methods.
Proposes a method for forecasting large-scale interval-valued time series.
problem Modeling and forecasting large-scale interval-valued time series.
method Feature extraction procedure involving auto-segmentation, clustering, and precision matrix estimation.
result The method enhances forecasting performance for large-scale interval-valued time series.
MEC improves efficiency and robustness in semi-supervised inference.
problem Efficient inference with limited labeled data and robust uncertainty quantification.
method Machine-Learning-Assisted Generalized Entropy Calibration (MEC) using cross-fitted, calibration-weighted PPI.
result MEC achieves semiparametric efficiency bounds under weaker assumptions and provides near-nominal coverage.