CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
problem Lack of principled uncertainty quantification in one-shot prediction.
method CAOS, a conformal framework that aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme.
result CAOS produces smaller prediction sets with reliable coverage compared to split conformal baselines.
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.
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.
Adaptive Bayesian learning aggregates experts to improve performance.
problem Bayesian online learning's performance depends on inferential choices.
method Treat Bayesian update rules as experts and aggregate them based on sequential predictive losses.
result The aggregate competes with the best expert in hindsight at a low aggregation cost.
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.
A conformal procedure improves CoT reasoning by aggregating reasoning paths and calibrating abstention rules.
problem Aggregation uncertainty in chain-of-thought reasoning makes correct answers less reliable.
method Introduces a conformal procedure for CoT reasoning that uses weighted score aggregation and abstention rules.
result Achieves higher selective accuracy with abstention, reducing confident-error rate.
COMA combines prediction sets from multiple models for online, adaptive prediction.
problem Combining multiple prediction models with uncertainty guarantees.
method Online model aggregation using weighted voting of conformal prediction sets.
result COMA retains coverage guarantees under negative correlation assumptions.
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. In this paper, we explore the application of making predictions over multiple data sources of different sizes without disclosing data between the sources. We propose that each data source applies a trans…
This paper tackles hidden state inference for HMMs using particle filtering.
problem Inference for hidden states under HMMs is challenging due to unavailable true labels.
method Adaptive conformal inference framework using particle filtering.
result The framework produces prediction sets with specific aggregated coverage levels.
Method constructs uniformly valid prediction sets across multiple distributions.
problem Uniformly valid prediction sets across multiple distributions.
method Max-p aggregation scheme and optimization programs.
result Optimal and efficient prediction sets for multiple distributions.
This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.
problem Generating accurate predictive sets with guaranteed coverage for multi-input classification tasks.
method Integrates p-values from each observation to reduce the size of the predicted label set while maintaining class-conditional coverage.
result The method reduces the size of the predicted label set while preserving the required coverage guarantee.
Adaptive Conformal Inference improves time series forecasting uncertainty.
problem Uncertainty quantification in time series models with dependency.
method AgACI, an adaptive method based on online expert aggregation.
result AgACI provides efficient prediction intervals for day-ahead electricity price forecasting.
We improve prediction set coverage by assigning weights to individual sets.
problem Aggregating multiple prediction sets weakens overall coverage guarantee.
method Propose a framework for weighted aggregation of prediction sets.
result Achieve tighter coverage bounds that interpolate between 1−2α and 1−α guarantees. Study aggregation of statistical evidence under unknown dependence using group-invariance.
problem Aggregating statistical evidence under unknown and complex dependence structures.
method Develops a framework using group-invariance and permutation-based constructions to aggregate evidence across transformed datasets.
result Shows uniform improvement in critical values for single-batch aggregation over deterministic calibrations, adapting to unknown dependence structures.
A new federated learning framework for handling device heterogeneity.
problem Handling device heterogeneity in federated learning.
method Superquantile-based objective with parameterized levels of conformity, optimized using secure aggregation.
result The optimization algorithm converges to a stationary point.
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.
The paper tackles uncertainty quantification in multi-source settings.
problem Uncertainty quantification under covariate shift is challenging in multi-source settings.
method The paper addresses this by proposing two extensions of weighted conformal prediction: merge-based aggregation and data-pooling.
result Theoretical guarantees are provided for the proposed approaches, and experiments validate their effectiveness.
Improves conformal prediction by combining multiple score functions and optimizing weights.
problem Limitations of single-score conformal predictors in multi-class classification.
method Combines multiple score functions and optimizes weights to minimize prediction set size.
result Consistently outperforms single-score conformal predictors while maintaining valid coverage.
Improves quantile regression models by aggregating multiple models.
problem Quantifying uncertainty and modeling diverse populations in predictions.
method Flexible model aggregation using weighted ensembles and modern deep learning.
result Improves accuracy and robustness of quantile predictions.
Community moderation drifts towards majority, study finds.
problem How to ensure crowd-sourced moderation systems trust and reward accurate evaluations.
method Consensus-based auditing with a two-stage algorithm that weights contributors by the stability of their past residuals.
result Minority contributors' evaluations drift towards the majority, and their participation share falls on controversial topics.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
The paper introduces localized conformal p-values for conditional testing problems.
problem Addressing conditional testing problems in statistics.
method Localized conformal p-values defined by inverting prediction intervals.
result Proposes procedures for conditional outlier detection and label screening with FDR and FWER control.
The yearly aggregated tax income data of all, more than 8000, Italian municipalities are analyzed for a period of five years, from 2007 to 2011, to search for conformity or not with Benford's law, a counter-intuitive phenomenon observed in large tabulated data where the occurrence of numbers having smaller initial digi…
A new method for optimizing hyperparameters using conformalized quantile regression.
problem Optimizing hyperparameters with strong assumptions about noise.
method Conformalized quantile regression for more realistic modeling.
result Quicker convergence on empirical benchmarks.
Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data. While the traditional description of conformal prediction starts with a nonconformity score, we provide an alternate (but equivalent) view…
UTOPIA aggregates multiple prediction intervals efficiently.
problem Constructing optimal prediction intervals for various real-world data problems.
method UTOPIA is a universally trainable strategy using linear or convex programming.
result UTOPIA constructs prediction intervals with small average width and high coverage probability.
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.
PRISM-FCP improves federated prediction robustness against Byzantine attacks.
problem Byzantine attacks in federated learning.
method Partial model sharing and distance-based maliciousness scores.
result Maintains nominal coverage guarantees under Byzantine attacks.
Framework controls uncertainty in LLMs without labels or probabilities.
problem Managing uncertainty in black-box LLMs without token-level probability or true labels.
method Integrates generative models, UCP, and conformal alignment to control uncertainty.
result Achieves close-to-nominal coverage and tighter thresholds than split UCP.
A mechanism to share risks and costs with guarantees against extreme outcomes.
problem Softening extreme individual burdens in risk sharing schemes.
method Formalizes Certified Allocation Problem; uses Conformal Risk Sharing with interpretable sharing policy and split conformal calibration.
result Reduces extreme obligations for high-risk agents while controlling harm to others.
New methods protect privacy while providing accurate prediction sets.
problem Privacy-preserving conformal prediction for untrusted aggregators.
method Two LDP approaches: k-ary randomized response and binary search response.
result Finite-sample coverage guarantees and robust coverage under randomization.
New method improves conformal prediction for ambiguous labels.
problem Uncertainty in expert voting labels affects CP guarantees.
method Develops Monte Carlo CP procedures approximating true posterior.
result Monte Carlo CP closes coverage gap with expert annotations.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
The paper introduces algorithms for uncertainty quantification in metric spaces.
problem Uncertainty quantification in regression models defined on metric spaces.
method Proposes conformal and kNN prediction algorithms for metric spaces.
result Both algorithms provide finite-sample guarantees and improve local coverage calibration.
New method uses label-weighted conformal prediction for macro-coverage guarantees in classification.
problem Finding a balance between class-conditional and marginal coverage in long-tailed datasets.
method Label-weighted conformal prediction for macro-coverage guarantees.
result Validated prediction sets with macro-coverage guarantees on large-scale image datasets.
Proposes a method to improve GNN predictions by finding the most predictive subgraph.
problem GNNs aggregate all nodes and edges, making predictions hard to interpret.
method Uses reinforcement learning to find a sparse subgraph that optimizes graph classification performance.
result Our method finds sparser subgraphs that improve interpretability while maintaining performance.
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
problem Accurately predicting DER adoption in electric grids with uncertainty and spatial disparity.
method Multivariate Hawkes process for modeling DER adoption dynamics and split conformal prediction algorithm for hierarchical validity.
result Empirical evaluation shows superior predictive accuracy and uncertainty calibration compared to existing methods.
Individual choices are either based on personal experience or on information provided by peers. The latter case, causes individuals to conform to the majority in their neighborhood. Such herding behavior may be very efficient in aggregating disperse private information, thereby revealing the optimal choice. However if …
Hierarchical framework for model evaluation on leaderboards
problem Uncertainty and variability in model performance across tasks
method Hierarchical framework with task-level and leaderboard-level rank prediction intervals
result Statistically valid and informative model rank intervals
Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.
problem Conformal prediction fails to cover minority classes in imbalanced datasets, leading to poor performance on rare labels.
method Class-conditional conformal prediction to improve coverage of minority classes.
result Class-conditional conformal prediction restores minority coverage to target with a modest increase in prediction-set size.
MMDCP improves outlier detection and classification with adaptive prediction sets.
problem Label shift and distribution differences in multi-class settings.
method Combines distance measures with full conformal prediction for adaptive prediction sets.
result Valid coverage and effective control of class-wise false discovery rate (CW-FDR).
A new method for batch prediction sets in classification problems.
problem Constructing reliable prediction sets for multiple unlabeled examples.
method Proposes a uniformly more powerful approach to batch prediction sets using specific combinations of conformal p-values.
result The proposed method provides narrower prediction sets compared to the Bonferroni correction.
SAGA predicts multi-year earnings with adaptive intervals, improving forecast accuracy.
problem Forecasting long-range nonlinear structure in lifetime earnings.
method Decoder-only transformer for irregular tabular sequences, split conformal calibration.
result Significant improvement in forecast accuracy compared to existing methods.
Study shows improper learning can outperform proper learning in misspecified models.
problem Misspecification in probabilistic prediction models.
method Investigates the performance of proper and improper learning strategies in misspecified models.
result Improper learning can achieve lower regret compared to proper learning, especially in high-dimensional settings.
SCIENCE improves prediction intervals for individual causal effects.
problem Wide prediction intervals limit practical utility of causal inference.
method Surrogate-assisted conformal inference for efficient individual causal effects.
result SCIENCE produces more efficient prediction intervals for individual causal effects.
Proposes a new optimization-based method for aggregating sets in neural networks.
problem Limited representational power of existing aggregation methods.
method Equilibrium Aggregation: an optimization-based approach.
result Equilibrium Aggregation outperforms existing methods in various tasks.
CSP improves time-series forecasting without training, outperforming DeepNPTS in speed and accuracy.
problem Improving probabilistic time-series forecasting without training.
method Mixing empirical and residual draws around a seasonal naive forecast.
result CSP significantly outperforms DeepNPTS on CRPS, normalized mean quantile loss, and coverage metrics.
Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.
problem Quadratic overhead in secure model aggregation for federated learning.
method Multi-group circular strategy, additive secret sharing, and coding techniques.
result Achieves O(NlogN) overhead, compared to O(N2), for up to 50% user dropout.