Label Shift

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

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Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores
Sep 14, 2026Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba +2Label Shift Conformal Prediction
MOGAM Institute for Biomedical Research, Korea · CROID Research and aSSIST University, Korea
From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift
Sep 13, 2026Mushan Li, Kihyun Han, Yanyuan MaLabel Shift Confidence Region Estimation
Department of Statistics, The Pennsylvania State University University Park, PA 16802, USA
Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope
Aug 10, 2026Michaël Dell'aiera, Thomas Vuillaume, Alexandre BenoitClass-Imbalanced Learning Label Shift
CNRS LAPP, Annecy, 74940, France · Université Savoie Mont Blanc LISTIC, Annecy, 74940, France
Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG
Aug 5, 2026Shiwen Chu, Shanglin Li, Motoaki Kawanabe +1Cross-Subject EEG Decoding Class-Imbalanced Learning
Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto, Japan · BIFOLD – Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany · Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, Japan
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Jul 6, 2026Alejandro Moreo, Pablo González, Juan José del CozLabel Shift Bayesian Inference
Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa, Italy · Artificial Intelligence Center University of Oviedo Asturias, Spain
Label Shift Aware Adaptation for Online Zero-shot Learning with Contrastive Language-Image Pre-Training (CLIP)
Jun 13, 2026Pengxiao Han, Changkun Ye, Yanshuo Wang +5Source-Free Domain Adaptation Zero-Shot Learning
Australian National University, Canberra, ACT, Australia · China North Vehicle Research Institute, Beijing, China · The Hong Kong Polytechnic University, Hong Kong SAR, China +3
Polyp-D2ATL: Deep Domain-Adaptive Transfer Learning for Colorectal Polyp Classification under Label Distribution Shift
Jun 12, 2026Sajad Jabarzadeh Ghandilu, Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi +1Class-Imbalanced Learning Label Shift
School of Electrical Engineering, Sharif University of Technology, Tehran, Iran · School of Electrical Engineering, Iran University of Science and Technology,2026 Tehran, Iran
Anytime-Valid Confirmation of Label-Shift Corrections
Jun 12, 2026Seungjin ChoiLabel Shift Anytime-Valid Inference
CROID Research and aSSIST University, Seoul, Korea.
Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Jun 10, 2026Seungjin ChoiWeighted Conformal Prediction Label Shift
1CROID Research and aSSIST University, Seoul, Korea.
Characterizing and Correcting Effective Target Shift in Online Learning
May 8, 2026Ziyan Li, Naoki HirataniLabel Shift Kernel Regression
Department of Physics Washington University in St. Louis St. Louis, USA · Department of Neuroscience Washington University in St. Louis St. Louis, USA
Locality-aware Private Class Identification for Domain Adaptation with Extreme Label Shift
May 7, 2026Chuan-Xian Ren, Cheng-Jun Guo, Hong YanLabel Shift Unsupervised Domain Adaptation
School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China · Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment
May 6, 2026Seunghan LeeDistribution Shift Robustness Tabular Foundation Models
LG AI Research, Seoul, South Korea.
Factorizable joint shift revisited
Jan 21, 2026Dirk TascheDistribution Shift Robustness Label Shift
Centre for Business Mathematics and Informatics, North-West University, South Africa

Sep 14, 2026, cs.LGJ/K move · Enter open · S save

Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores

Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi+1

MOGAM Institute for Biomedical Research, Korea · CROID Research and aSSIST University, Korea

Label Shift Conformal Prediction Regression Prediction Intervals

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Conformal prediction provides distribution-free uncertainty quantification under exchangeability. However, this assumption is violated by label shift, where the marginal distribution of labels changes while the conditional distribution of inputs given labels remains stable. Under such shifts, standard conformal procedures no longer maintain their intended coverage behavior. Existing approaches address this via importance weighting. They pair the reweighting with residual-based nonconformity scores that ignore predictive uncertainty. The resulting intervals have uniform width. Bayesian conformal methods produce adaptive intervals by leveraging predictive distributions. They evaluate conformity under the source predictive, which is misaligned with the target domain under label shift. We propose the \emph{Label-Shift-Adjusted Bayesian Score} (LSA score), a nonconformity score derived from a posterior predictive tilting identity. This identity shows that the target predictive is an importance-weighted transformation of the source predictive. We use it to derive a direct correction to the Bayesian score. We evaluate the method on molecular property prediction under controlled label shift. The LSA score consistently yields shorter intervals than residual-based and source-based Bayesian scores. Coverage in the target domain remains comparable. Under stronger shift, all methods incur some coverage loss due to pseudo-label-based density-ratio estimation. The LSA score is defined for any source predictive with a tractable log-density. We instantiate it with Bayesian Ridge Regression, where the correction admits a closed form.

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