Proposes SGShift to identify shifted features causing model performance degradation under concept shift.
problem Concept shift leading to miscalibration in ML models across domains.
method SGShift method for identifying sparse set of shifted features using feature selection and statistical tools.
result SGShift identifies shifted features more accurately than baseline methods, requires few samples in the shifted domain, and is robust to complex cases.
FSL-Net detects and localizes feature shifts in large, high-dimensional datasets.
problem Feature shifts between data sources lead to erroneous features in various applications.
method FSL-Net is a neural network trained on multiple datasets to localize feature shifts.
result FSL-Net accurately localizes feature shifts from unseen datasets without re-training.
This work uses adversarial learning to detect and correct feature shifts in various datasets.
problem Detecting and correcting feature shifts in real-world datasets.
method Adversarial learning applied to multiple discriminators to detect and correct feature shifts.
result Mainstream classifiers can effectively localize and correct feature shifts, outperforming existing techniques.
Detects domain shifts in datasets using interpretable feature subspaces.
problem Detecting subtle differences in dataset probability distributions.
method Localised density anomaly detection in high-dimensional feature spaces.
result Extracts interpretable feature subspaces for domain shifts.
A feature-weighted mean shift algorithm improves clustering in high-dimensional data.
problem Clustering high-dimensional data with traditional mean shift algorithms.
method Feature-weighted mean shift algorithm.
result The algorithm outperforms conventional mean shift and preserves computational simplicity.
Proposes FSM-IRL to learn invariant network representations considering feature and structural shifts.
problem Spatial heterogeneity and temporal dynamics lead to OOD generalization issues in geographic networks.
method Introduces FSM-IRL model that accounts for feature and structural distribution shifts using causal attention and reweighting.
result Demonstrates strong learning capabilities on geographic and social network datasets in OOD scenarios.
This paper introduces LR-FFS for robust feature screening in federated learning under label shift.
problem Label shift challenges in federated learning for high-dimensional classification.
method Unified feature screening framework, label-shift robust federated feature screening (LR-FFS), federated estimation procedure.
result LR-FFS outperforms existing methods in diverse client environments with varying class distributions, sample sizes, and missing data.
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.
Detects which features have shifted in data distributions.
problem Identifying which specific features have caused a distribution shift.
method Formalizes the problem as multiple conditional distribution hypothesis tests, proposes non-parametric and parametric statistical tests, and uses a test statistic based on the density model score function.
result Demonstrates methods for identifying when and where a shift occurs in multivariate time-series data.
New framework learns sufficient invariant features robustly across distribution shifts.
problem Learning robust models under distribution shifts between training and test datasets.
method Sufficient Invariant Learning (SIL) framework and Adaptive Sharpness-aware Group Distributionally Robust Optimization (ASGDRO) algorithm.
result Empirical evaluations confirm ASGDRO's robustness against distribution 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.
Paper quantifies label shift with robustness guarantees using distribution feature matching.
problem Estimating target label distribution under label shift.
method Distribution feature matching (DFM) framework and robustness analysis.
result General performance bound and robustness analysis in misspecified settings.
New insights into how high-dimensional models handle covariate shifts.
problem Covariate shift in high-dimensional random feature regression.
method Exact high-dimensional asymptotics of random feature regression under covariate shift.
result Overparameterized models exhibit enhanced robustness to covariate shift.
New CPS model tackles conditional probability shift in machine learning.
problem Discrepancy between source and target distributions in machine learning.
method Conditional Probability Shift Model (CPSM) using multinomial regression and EM algorithm.
result Superior balanced classification accuracy on target data compared to existing methods.
Method selects features robust to concept shift using Shapley values.
problem Feature selection in static data does not work well with concept shifts.
method Establishes a direct relationship between Shapley values and prediction errors, detecting individual variable biases.
result Significantly outperforms state-of-the-art feature selection methods in concept shift scenarios.
Self-training avoids spurious features in domain adaptation.
problem Domain shift with large differences between source and target domains.
method Entropy minimization on unlabeled target data, initialized with a source classifier.
result Entropy minimization avoids using spurious features in large domain shifts.
A new method for adapting to label shifts using class probability matching.
problem Adapting to label shifts where class probabilities differ between source and target domains.
method Class Probability Matching using Kernel Methods (CPMKM) framework.
result CPMKM outperforms existing methods on real datasets.
PRoFILE accurately estimates feature importance under distribution shifts.
problem Challenges in estimating feature importance under distribution shifts.
method PRoFILE uses a loss estimator trained with a causal objective to estimate feature importance.
result PRoFILE accurately estimates feature importance under complex distribution shifts.
FIT evaluates time series model feature importance quantifying distributional shift.
problem Lack of explanations for time series models in high-stakes applications.
method FIT framework quantifies feature importance based on distributional shift using KL-divergence.
result FIT identifies important time points and observations superiorly compared to baselines.
Safe-DRFS selects features robust to covariate shifts for reliable performance.
problem Feature selection fails in diverse deployment environments.
method Safe-DRFS extends safe screening to distributionally robust settings under covariate shift.
result Safe-DRFS identifies a feature subset encompassing optimal subsets across distribution shifts.
This paper explores how effective sample size, dimensionality, and model performance are related in covariate shift adaptation.
problem Understanding the relationship between effective sample size, dimensionality, and generalization in covariate shift adaptation.
method Building a unified theory connecting effective sample size, data dimensionality, and generalization in the context of covariate shift adaptation.
result Dimensionality reduction or feature selection can increase effective sample size, supporting the practice of reducing dimensionality before covariate shift adaptation.
Conformal prediction fails under severe feature turnover in COVID-19 supply chain tasks.
problem Dealing with distribution shift in conformal prediction models.
method Using COVID-19 as a natural experiment across 8 supply chain tasks, analyzing SHAP explanations.
result Coverage drops vary widely (0% to 86.7%) and correlate with single-feature dependence.
Paper proposes SJS model to estimate model performance under covariate and label shifts.
problem Estimating model performance when both covariates and labels shift.
method Sparse Joint Shift (SJS) model and SEES algorithm.
result SEES achieves significant shift estimation error improvements over existing approaches.
FJS method improves multinomial classification accuracy.
problem Improving multinomial classification accuracy under dataset shift.
method Derive FJS representation and propose alternative methods.
result Factorizable joint shift is not fully identifiable without additional assumptions.
New method restores source features for SFDA without source data.
problem Domain adaptation without access to source data.
method Feature Restoration (FR) and Bottom-Up Feature Restoration (BUFR).
result BUFR outperforms existing SFDA methods in accuracy, calibration, and data efficiency.
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…
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation …
DRSS method identifies unnecessary samples and features in DR covariate shift.
problem Identifying unnecessary samples and features in DR covariate shift.
method Combines DR learning and safe screening techniques.
result DRSS method provides reliable identification of unnecessary samples and features under specified distribution uncertainty.
Optimizes feature shifts for tree ensemble reclassification.
problem Improving tree ensemble reclassification accuracy through feature perturbation.
method Mathematical optimization of feature shifts to maximize reclassification probability.
result Effective feature ranking and optimization for tree ensemble reclassification.
The paper analyzes how machine learning models perform under covariate shift, especially when the feature shift in x is larger than that in y.
problem Performance of machine learning models under covariate shift with heterogeneous feature changes.
method Empirical risk minimization (ERM) over functions f+g, fit on a training distribution, evaluated on a test distribution with covariate shift. result ERM is more resilient to heterogeneous covariate shifts when the class F is simpler than G. Two-dimensional case in the theory of dynamical systems admitting the normal shift differs crucially from multidimensional case. Features of two-dimensional case are gathered and studied in this thesis.
Extends Shifts dataset for MS lesion segmentation and marine vessel power estimation.
problem Distributional shift in training and deployment data for ML models.
method Develops new datasets for high-risk industrial applications.
result Demonstrates robustness and uncertainty estimation in new industrial tasks.
The paper proves a new method to improve generalization in covariate-shift scenarios.
problem Improving performance on test distributions that differ from training distributions.
method Independence-driven importance weighting algorithms for feature selection.
result Theoretical proof that these algorithms can identify optimal variables for covariate-shift generalization.
AFA evaluates AI feature acquisition strategies in domains with high costs.
problem Evaluate AI feature acquisition strategies in domains with high costs.
method Apply missing data methods and offline reinforcement learning under NDE and NUC assumptions.
result Propose a novel semi-offline reinforcement learning framework with three new estimators.
The paper addresses instability in CNNs' first layer by proving max pooling's shift invariance.
problem Instability in CNNs' first layer, leading to sensitivity to small input shifts.
method Establishing conditions for max pooling's shift invariance and deriving a measure of stability.
result Max pooling approximates a nearly shift-invariant complex modulus under certain conditions.
New method helps interpret complex models by visualizing feature shifts.
problem Interpreting complex models like blackbox models is hard.
method Quantile Shift Method (QSM) to visualize feature shifts.
result Changes in predictions can be interpreted as neighborhoods of classes.
It has been shown that instead of learning actual object features, deep networks tend to exploit non-robust (spurious) discriminative features that are shared between training and test sets. Therefore, while they achieve state of the art performance on such test sets, they achieve poor generalization on out of distribu…
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
Batch normalization shifts models to rely more on non-robust features.
problem Understanding the impact of batch normalization on deep neural networks.
method Empirical analysis and a framework for disentangling robustness and usefulness.
result Batch normalization increases reliance on non-robust features, decreasing adversarial robustness.
Graphs models are vulnerable to distribution shifts, which this work explains and mitigates.
problem Graph Neural Networks (GNNs) are susceptible to distribution shift, leading to performance degradation.
method Theoretical analysis quantifying conditional shift, proposing an approach to estimate and minimize it.
result The proposed approach demonstrates up to 10% absolute ROC AUC improvement under various distribution shifts.
Paper tackles robust classification under class-dependent domain shift.
problem Class-dependent domain shift in machine learning.
method Defined a simple optimization problem with an information theoretic constraint and solved it using neural networks.
result Demonstrated that the proposed method can learn robust classifiers that generalize well to unseen domains.
Method uses aggregate crop statistics to improve satellite-based crop type mapping.
problem Limited field-level crop labels for training satellite-based maps.
method Corrects classifier by accounting for shifts in crop type composition and feature means.
result Substantial improvements in overall classification accuracy, reducing misclassifications by 21.9% on average.
Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existing deep adaptation methods focus on reducing domain shift by matching marginal feature distributions through deep transformations on the inpu…
Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsup…
Over-parameterized models reduce Out-of-Distribution (OOD) generalization loss.
problem Understanding how over-parameterized models handle non-trivial distributional shifts.
method Investigating random feature models and examining non-trivial natural distributional shifts.
result Increasing model parameterization reduces OOD loss.
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.
New approach models how explanations shift with distribution changes.
problem Model performance drops with changing input data distributions.
method Models explanation shifts and compares them to state-of-the-art techniques.
result Modeling explanation shifts better detects out-of-distribution behavior.
AugLoss combines data augmentation and robust loss functions for robust DL models.
problem Robustness against noisy labels and feature distribution shifts.
method Unified data augmentation and robust loss functions.
result AugLoss achieves gains over previous methods in various real-world dataset corruptions.