Covariate Shift

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5 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 43

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Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting
Oct 1, 2026Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan +3Data Augmentation Covariate Shift
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281 USA
Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation
Sep 30, 2026Chin-Hung Huang, JooChul Lee, Huan HePosterior Covariate Shift
Department of Mathematics and Statistics Auburn University
Sharp training-conditional coverage for conformal prediction under covariate shift
Sep 27, 2026Mehrdad PournaderiOnline Conformal Prediction Covariate Shift
Mofid Securities
CVaR anchor regression protects against rare shifts
Sep 22, 2026Malte LondschienCovariate Shift Conditional-Value-At-Risk
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
Sep 21, 2026Rustam Isaev, Anton Conrad, Denis Belomestny +2Quantile Regression Covariate Shift
Faculty of Computer Science, HSE University, Moscow, Russia · Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia · Laboratoire de Recherche d’EPITA, Le Kremlin-Bicêtre, France +2
General Quantification of Covariate and Concept Shifts
Sep 12, 2026Hongbo Chen, Li Charlie XiaCovariate Shift Distribution Shifts
Department of Statistics and Financial Mathematics, School of Mathematics, South China University of Technology, Guangzhou, China.
Conformal Calibration Transfer
Sep 11, 2026Achref DoulaOnline Conformal Prediction Domain Adaptation
Technical University of Darmstadt, Darmstadt, Germany · Tongji University, Shanghai, China.
Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shift
Aug 11, 2026Jiamiao Liu, Dewen Qiao, Yu Zhang +1Certification Covariate Shift
Department of Information, Xinqiao Hospital Army Medical University (Third Military Medical University) Chongqing 400037, China
Nonparametric Goodness-of-fit Testing under Covariate Shift
Aug 5, 2026Zhen Hou, Dong XiaCovariate Shift Exponential Family
Department of Mathematics Hong Kong University of Science and Technology
Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
Aug 1, 2026Ying Jin, Ying Jin, Dominik RothenhäuslerCovariate Shift Importance Weighting
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
Jul 29, 2026Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari +1Medical World Model Covariate Shift
Institute of Business Administration Karachi, Karachi, Pakistan · Gachon University, Korea · St. John’s University, New York, USA
PIcsC: Partitioning-Induced Covariate Shift Correction
Jul 28, 2026Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1Covariate Shift Federated Learning
School of Mathematics and Computer Science, Institute of Business Administration Karachi, Pakistan. · Department of Computer Science, Habib University, Karachi, Pakistan. · Division of Computer Science, Mathematics and Science, St. John’s University, USA.
Smart predict-then-robustly-optimize
Jul 23, 2026Aakil Caunhye, Xuefei Lu, Belen Martin-BarraganRobust Optimization Predictive Uncertainty
Adaptive deep nonparametric regression from dependent data under covariate shift
Jul 22, 2026William Kengne, Ehud Mossa OckegnaCovariate Shift Exponential Family
Université Jean Monnet, ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Universite Claude Bernard Lyon 1, 42000 Saint-Étienne, France
The Label Complexity of Class-Conditional Coverage under Distribution Shift
Jul 20, 2026Weijia Han, Lisha QuCovariate Shift Online Conformal Prediction
University of Washington
Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
Jul 18, 2026Nadine Chang, Maying Shen, Shizhe Diao +6Post-Deployment Adaptation Data-Curation
NVIDIA · NVIDIA & University of Ottawa
A Geometry-Limited Identification Floor and Its Consequences for Voice-Clone Attribution in Professional Voice Actors
Jul 17, 2026Shuhei KatoCross-Lingual Voice Cloning Speech Encoder
The author is an independent researcher in Tokyo, Japan.
Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
Jul 7, 2026Gunner Levi HoweSelection Bias Importance Weighting
Independent Researcher
Group-invariant Coresets for Data-efficient Active Learning
Jul 1, 2026L. C. Ayres, J. C. M. Bermudez, S. J. M. de Almeida +1Geometry-Aware Uncertainty Coresets Pool-Based Active Learning
Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil · Universidade Católica de Pelotas, Pelotas, RS, Brazil · Université de Lorraine, CNRS, CRAN, Vandoeuvre-lès-Nancy, France
Target-Aware Linear Regression Under Distribution Shift
Jun 22, 2026Zhewen Hou, Tian ZhengCovariate Shift Distribution Shifts
Department of Statistics Columbia University New York, NY
Convergence Analysis of Nyström Subsampling in Covariate Shift Adaptation for Misspecified case
Jun 20, 2026Hanna Myleiko, Sergei Solodky, Vasyl SemenovCovariate Shift Domain Adaptation
Institute of Mathematics NAS of Ukraine, 3 Tereschenkivska st., Kyiv, Ukraine. · Institute of Mathematics NAS of Ukraine, 3 Tereschenkivska st., Kyiv, Ukraine · University of Giessen, Department of Mathematics, Giessen, Germany. +1
PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMs
Jun 18, 2026Abhijit Das, Nichula Wasalathilaka, Yifan Lu +4Medical Vision-Language Models Vision-Language Model Adaptation
MBZUAI, Abu Dhabi, UAE · University of Peradeniya, Sri Lanka · Khalifa University, Abu Dhabi, UAE +2
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Jun 12, 2026Annie Ulichney, Amanda CostonCovariate Shift Selection Bias
Department of Statistics, University of California, Berkeley
Anytime-Valid Confirmation of Label-Shift Corrections
Jun 12, 2026Seungjin ChoiCovariate Shift Conformal Test Martingales
CROID Research and aSSIST University, Seoul, Korea.
Off-Policy Evaluation with Strategic Agents via Local Disclosure
Jun 5, 2026Kiet Q. H. Vo, Abbavaram Gowtham Reddy, Julian Rodemann +2Off-Policy Learning Strategic Agents
Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Relational Machine Learning Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Department of Statistics, LMU Munich, Germany +1
From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
May 29, 2026Firas Gabetni, Alexandre Rocchi Henry, Nacim Belkhir +2Covariate Shift Positive--Unlabeled Learning
U2IS, ENSTA · Institut Polytechnique de Paris · AMIAD, Pôle Recherche, Palaiseau
Multicalibration Boosting: Theory, Convergence, and Transferability
May 23, 2026Hanxuan Ye, Hongzhe LiOnline Conformal Prediction Covariate Shift
Department of Biostatistics and Epidemiology University of Pennsylvania Philadelphia, PA 19104-6021, USA · Department of Biostatistics and Epidemiology University of Pennsylvania2026 Philadelphia, PA 19104-6021, USA
Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
May 20, 2026Jinzong Dong, Zhaohui Jiang, Bo YangCovariate Shift Confidence Estimation
School of Automation, Central South University, Changsha, China.
Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
May 17, 2026Paulo Akira F. Enabe, Rodrigo ProvasiCovariate Shift Pac-Bayesian Theory
Escola Politénica University of São Paulo Department of Structural and Geotechnical Engineering
TILT: Target-induced loss tilting under covariate shift
May 14, 2026Kakei Yamamoto, Martin J. WainwrightCovariate Shift Domain Adaptation
Lab for Information and Decision Systems · Statistics and Data Science Center · EECS† +2
Learning Large-Scale Modular Addition with an Auxiliary Modulus
May 8, 2026Hanato Kikuchi, Ryosuke Masuya, Kazuhiko Kawamoto +1Modular Framework Covariate Shift
Chiba University · National Institute of Informatics
DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
May 7, 2026Kateryna Husar, Alexander VolfovskyCovariate Balancing Bayesian Experimental Design
Department of Statistical Science Duke University Durham, NC 27705
Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
May 7, 2026Steven Wilkins-Reeves, Alexandra N. M. Darmon, Deeksha SinhaCovariate Shift Causal Inferences
Central Applied Science, Meta · New York, NY, USA · Menlo Park, CA, USA
Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift
May 6, 2026Jonathan R. LandersJacobian Covariance
Independent Researcher · New York, NY, USA
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
May 3, 2026James Wang, Surbhi GoelConformal Inference Nonconformity Scores
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Multicalibration for Unbiased Model-Based Prevalence Estimation
Apr 23, 2026Fridolin Linder, Thomas Leeper, Daniel Haimovich +3Prevalence Covariate Shift
Meta Platforms Inc. · The London School of Economics and Political Science
T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World
Mar 19, 2026Aditi Naiknaware, Salimeh SekehVision-Language Model Adaptation Out-Of-Distribution Detection
San Diego State University
ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models
Feb 27, 2026Wei Luo, Yangfan Ou, Jin Deng +4Vision-Language Model Adaptation Recent Vision-Language Models
South China Agricultural University · Pazhou Laboratory · South China University of Technology +1
Sequential Cohort Selection under Uncertainty
Aug 22, 2025Hortence Yiepnou, Christos DimitrakakisAlgorithmic Fairness Covariate Shift
University of Neuch atel
Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients
Feb 26, 2025Martin Surner, Abdelmajid Khelil, Ludwig BothmannAdversarial Training Covariate Shift
Institute for Data and Process Science, Landshut University of Applied Sciences, Landshut, Germany. · Computer Science Department, Landshut University of Applied Sciences, Landshut, Germany. · Department of Statistics, LMU Munich, Munich, Germany. +1
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Sep 25, 2024Alexander Popov, Alperen Degirmenci, David Wehr +9Autonomous Driving Imitation Learning
NVIDIA
Generalization error of min-norm interpolators in transfer learning
Jun 20, 2024Yanke Song, Kenneth Gu, Sohom Bhattacharya +1Stochastic Interpolants Empirical Risk Minimization
Department of Statistics, Harvard University · Department of Statistics, Stanford University · Department of Statistics, University of Florida
Guided Adversarial Robust Transfer Learning with Source Mixing
Date pendingXin Xiong, Zijian Guo, Tianxi CaiTransfer Learning Adversarial Training
Harvard T.H. Chan School of Public Health, Boston, MA, USA · Center for Data Science, Zhejiang University, Zhejiang, China · Harvard Medical School, Boston, MA, USA

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Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan, Kunpeng Liu+2

School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281 USA

Data Augmentation Covariate Shift Reweighting Generative Models

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In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between training and deployment (covariate shift); 3) validation sets often diverge from unseen test environments; or 4) standard generative models simply mimic outdated source distributions. This learning setting limits the stability of standard augmentation and adaptation pipelines. We generalize the task under such setting as the Augmented and Weighted Learning under Covariate Shift problem (AWL-CS). AWL-CS imposes two critical challenges on existing methods: 1) misleading generative guidance where models optimize for source similarity rather than downstream task relevance, and 2) structural instability of distributional density where reweighting mechanisms overfit to noisy validation signals. To tackle these challenges, we propose IGDPR (Invariant-Guided Diffusion with Prototype Reweighting), a unified framework that synergizes stable synthesis and structural adaptation: i) To achieve task-relevant generation, we steer the diffusion sampling process using invariant potentials to ensure synthetic samples align with stable decision boundaries rather than outdated correlations. ii) To ensure stable adaptation, we develop a prototype-based reweighting strategy that assesses sample reliability through structural clusters instead of isolated points, effectively filtering validation noise. Extensive experiments on real data demonstrate our method improves data quality by augmenting the most beneficial data for robust learning.

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