This research examines how model explanations change under distribution shifts in tabular data.
problem Detecting distribution shifts in tabular data affecting model performance and explanations.
method Investigates the relationship between model performance and explanation characteristics under distribution shifts.
result Explanation shifts are a better indicator for detecting predictive performance changes than traditional distribution shift techniques.
New method learns distribution shifts caused by predictive models in social computing.
problem Learning distribution shifts due to predictive models in social computing.
method Reverse causal model with microfoundation for agents' actions.
result Effective in minimizing performative prediction risk.
This paper improves conformal prediction for robust interval estimation under distribution shifts.
problem Robustness of conformal prediction under distribution shifts.
method Modeling distribution shifts using Levy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations.
result Constructs robust conformal prediction intervals that remain valid under distribution shifts.
DS-CP improves reliability of uncertainty quantification for large language models under domain shift.
problem Overconfident and factually incorrect outputs (hallucinations) from large language models.
method Adapts conformal prediction to large language models under domain shift by reweighting calibration samples.
result DS-CP delivers more reliable coverage than standard conformal prediction, especially under substantial distribution shifts.
Improved vehicle motion prediction with uncertainty estimation.
problem Robust motion prediction for autonomous vehicles, especially under distributional shift.
method Presented an approach significantly improving the benchmark and taking 2nd place on the leaderboard.
result Significantly improved motion prediction and uncertainty measurement.
WCPS extends CPS to handle covariate shifts, providing probabilistically calibrated predictions.
problem Applying CPS to scenarios with covariate shifts.
method WCPS uses likelihood ratios between training and testing covariate distributions.
result WCPS are probabilistically calibrated under covariate shift.
Paper improves prediction sets for distribution shifts without labels.
problem Improving prediction sets effectiveness in the presence of distribution shifts.
method Develops ECP and EACP methods to adjust score function based on model uncertainty.
result Consistent improvement over existing baselines and nearly matches fully supervised methods.
Extend CPS to non-exchangeable settings with observation-specific permutation weights
problem Calibrated predictive bands under distributional shifts
method Encoding distributional shifts through observation-specific permutation weights
result Shift-aware predictive systems remain valid
Algorithm calibrates predictions for covariate shift using domain adaptation.
problem Uncertainty estimates overestimate certainty when real-world data differs from training data.
method Uses importance weighting and learns a feature map to equalize distributions.
result Outperforms existing approaches in calibrated prediction when covariate shift occurs.
Anytime-valid confirmation of label-shift corrections
problem Small-batch scientific deployments with scarce labeled outcomes
method Conditional e-value and martingale-based rule
result Nonnegative martingale and anytime-valid confirmation rule
Proposes a method to quantify uncertainty in predictions under covariate shift.
problem Uncertainty quantification challenges in machine learning with covariate shifts.
method Constructs PAC prediction sets with given importance weights and confidence intervals for weights.
result Algorithm gives prediction sets with the smallest average normalized size.
Bayes predictor remains robust to ignorable missingness shifts.
problem Challenges in prediction with missing covariates and shifts in missingness reasons.
method Bayesian approach and different prediction methods.
result Bayes predictor remains unchanged by ignorable shifts, but robust prediction requires disregarding missingness for non-ignorable shifts.
CTRF combines logged data and randomized experiments for robust prediction.
problem Robust prediction models to handle distributional shifts between training and testing data.
method CTRF uses existing training data and a small amount of randomized experiment data to train a robust model.
result CTRF produces robust predictions and outperforms baseline methods in the presence of feature shifts.
ShapShift explains shifts in model predictions due to data distribution changes.
problem Prediction shifts caused by changes in input distribution.
method Subgroup Conditional Shapley Values applied to decision trees and ensembles.
result Simple, faithful, and near-complete explanations of prediction shifts across model classes.
Study detects concept shift in online data using martingales.
problem Detecting concept shift in online datasets.
method Exchangeable martingales and conformal prediction techniques.
result Decomposes concept shift into detectable components.
CP2 uses geometric information to improve conformal prediction robustness.
problem CP fails under geometric data shifts, losing coverage guarantees.
method Integrates geometric pose information into CP via canonicalization.
result Integrating geometric information with CP ensures robustness under geometric shifts.
WR-CP reduces prediction set size and coverage gap under distribution shift.
problem Guaranteed coverage under distribution shift not achievable with i.i.d. assumption.
method Wasserstein distance, probability measure pushforwards, importance weighting, regularized representation learning.
result Reduces coverage gap to 3.2% across different confidence levels.
Partially performative prediction studies how predictive models influence future data.
problem Distribution shift in predictive models due to endogenous and exogenous factors.
method Generalizing performative prediction to capture both endogenous and exogenous sources of distribution shift.
result Developed online analogues of performative stability and optimality for partially performative environments.
Paper tackles distribution shifts in prediction models with unobserved confounding.
problem Distribution shifts in prediction models with unobserved confounding.
method Linear structural causal model, invariant covariate representations, data-driven representation learning method.
result Optimizes for a lower-dimensional linear subspace and a prediction model confined to that subspace, achieving nearly ideal gap between target and source risk.
New framework identifies worst-case shifts for predictive resource allocation models.
problem Identifying harmful shifts in predictive models for resource allocation.
method Hierarchical model structure and submodular optimization for worst-case loss.
result Empirical evidence shows divergent worst-case shifts identified by different metrics.
CoDrug uses KDE to create valid prediction sets for drug molecules under covariate shift.
problem Creating reliable uncertainty estimates for drug properties from computational models.
method CoDrug employs an energy-based model and KDE to assess and rectify distribution shift.
result CoDrug reduces the coverage gap by over 35% compared to non-adjusted conformal prediction sets.
New method robust to random distributional shifts in prediction.
problem Random distributional shifts in real-world settings.
method Hybrid approach combining long-term and proxy outcomes.
result Hybrid approach yields lower mean-squared error than current methods.
Fairness measures fail in predictive settings that intentionally shift outcomes.
problem Fairness measures fail in performative prediction settings.
method Formalized concept shift and counterfactual outcomes.
result Predictors that are fair during training become unfair during deployment.
Analysis of ridge regression under concept shift reveals nontrivial effects on generalization performance.
problem Understanding and mitigating the impact of distribution shift in machine learning models.
method Derivation of exact prediction risk expression in the thermodynamic limit for ridge regression under concept shift.
result Reveals a phase transition and nonmonotonic data dependence of test performance under concept shift.
ReVol normalizes stock price features to mitigate distribution shifts, improving prediction accuracy.
problem Distribution shifts in stock price data hinder accurate prediction.
method ReVol uses normalization, attention-based estimation, and geometric Brownian motion.
result ReVol achieves an average improvement of more than 0.03 in IC and over 0.7 in SR.
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.
New methods adapt conformal prediction to unknown subpopulation shifts.
problem Failure of conformal prediction under unknown subpopulation shifts.
method Proposes new methods that adapt conformal prediction to unknown subpopulation shifts without explicit subpopulation labels.
result Ensures valid coverage guarantees without explicit knowledge of subpopulation structure.
New method predicts sets under unknown covariate shift with high confidence.
problem Adapting to unknown covariate shift in prediction sets.
method PredSet-1Step, a flexible distribution-free method.
result Achieves asymptotic probably approximately correct coverage.
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.
New federated conformal prediction method addresses label shift for uncertainty quantification.
problem Label shift in federated learning and its impact on uncertainty quantification.
method Quantile regression-based federated conformal prediction method with privacy constraints.
result Method provides valid coverage of prediction sets and differential privacy guarantees.
SJS model predicts label shifts in multinomial datasets.
problem Predicting label shifts in multinomial datasets.
method Sparse joint shift model for dataset shift.
result Valid predictions and class prior probabilities estimates.
New method uses optimal transport to improve conformal prediction under distribution shifts.
problem Improving conformal prediction's coverage in non-exchangeable settings.
method Optimal transport to estimate and mitigate distribution shifts.
result Estimates and mitigates loss in coverage for arbitrary distribution shifts.
Adaptive model learns from time series data with changing distributions.
problem Predicting time series data under distribution shift.
method Formulates distribution shift as weighted empirical risk minimization. Uses a gradient-based learning method for a forgetting mechanism.
result Proposes an efficient method for adaptive time series prediction.
FakeEdge tackles dataset shift in link prediction tasks.
problem Dataset shift between training and testing sets in link prediction.
method Model-agnostic technique to mitigate graph topological gap.
result Extensive experiments show FakeEdge's superiority on multiple datasets.
New method detects novel node categories in graphs with distribution shifts.
problem Detecting novel node categories in graphs with distribution shifts.
method Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP).
result RECO-SLIP outperforms existing methods in detecting novel node categories.
Paper proposes faster adaptation to distribution shifts in online settings.
problem Violation of exchangeability assumption in evolving data environments.
method Online conformal inference with retrospective adjustment.
result Faster adaptation to distributional shifts demonstrated through numerical studies.
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately, common methods for addressing covariate shift by trying to remove the bias between training and testing distributions using importance weigh…
The study improves model performance prediction on unseen distributions.
problem Improving model performance prediction on unseen distributions.
method Connecting domain adaptation and predictive uncertainty techniques, investigating distributional distances and DoC.
result Difference of confidences (DoC) successfully estimates classifier performance change over various distribution shifts.
Study evaluates conformal prediction methods for safety in vision models under shifts and long-tailed data.
problem Safety guarantees of conformal prediction methods under distribution shifts and long-tailed data.
method Empirical evaluation of post-hoc and training-based conformal prediction methods on large-scale datasets and models.
result Performance of conformal prediction methods degrades significantly under distribution shifts and long-tailed data.
Prediction-time batch normalization improves model robustness under covariate shift.
problem Dealing with covariate shift in deep learning models.
method Prediction-time batch normalization, a simple but effective method.
result Significantly improves model accuracy and calibration under covariate shift.
Bayesian framework improves uncertainty estimates under covariate shifts.
problem Neural networks' unreliable uncertainty estimates under covariate shifts.
method Adaptive prior conditioned on training and new covariates, amortized variational inference.
result Significantly improved uncertainty estimates under distribution shifts.
The paper addresses the reliability of conformal prediction under covariate shift.
problem Ensuring reliable prediction sets under covariate shift.
method Derives upper bounds on training-conditional coverage.
result Offers PAC guarantees for conformal prediction methods.
The paper tackles uncertainty quantification for classification under label shift without assuming i.i.d. data.
problem Uncertainty quantification for classification under label shift in non-i.i.d. settings.
method The paper uses conformal prediction and post-hoc binning for distribution-free UQ, and reweights these methods for label shift.
result The reweighted methods improve UQ performance under label shift, preserving coverage and calibration.
A method corrects feedback shift in predicting conversion rates with delayed feedback.
problem Delayed feedback leads to mislabeling of positive instances in training data.
method Uses importance weight approach to correct feedback shift.
result Proposed method outperforms existing methods in offline and online experiments.
Two approaches improve conformal Bayes for label shift, one post-hoc and one in-training.
problem Improving prediction sets for target domain under label shift.
method Two complementary approaches: post-hoc calibration and in-training adaptation.
result In-training adaptation achieves up to 43% width reduction at unchanged coverage.
Audited Conformal Prediction improves conditional coverage in pretrained models under distribution shift.
problem Uncertainty quantification for pretrained models under unknown distribution shift
method Leverages a small labeled dataset to train an audit model for marginal coverage, integrates outputs into conformal prediction framework
result Significantly higher conditional coverage than existing approaches
New approach predicts under latent shifts using high-dimensional images.
problem Prediction under latent subgroup shifts with high-dimensional observations.
method Recognition-parametrised model (RPM) for identifying causal latent structure.
result Successfully adapts predictions for high-dimensional image data.
Proposes BSSP to stabilize predictions in biased data.
problem Distribution shift between training and test data causes prediction instability.
method Balance-subsampled stable prediction (BSSP) algorithm based on fractional factorial design.
result Significantly improves prediction stability across unknown test data.