Importance Weighting

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4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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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
Mind Which Bird You Favour: Parameterizing Adequacy-Fluency Balance in Meta-Evaluation of Machine Translation
Sep 13, 2026Behzad Shayegh, Niloofar KazemiImportance Weighting MT Evaluation
Independent Researchers
Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Sep 12, 2026Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3Distribution Shift Importance Weighting
NTT, Inc.
From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles
Sep 7, 2026Sana Ebrahimi, Suraj Shetiya, Abolfazl AsudehImportance Weighting Bibliometric Analysis
University of Illinois Chicago · IIT Bombay
Balance of Benchmarks: Semantic Density Reweighting for Benchmark Multiplicity and Task-Conditioned Evaluation
Aug 30, 2026Jhen-Ke LinLLM Evaluation Benchmark Design
National Yang Ming Chiao Tung University
When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide
Aug 12, 2026Binshuang LiInverse Probability Weighting Causal Effect Estimation
Independent Researcher
SSPO: Structure-Aware Similarity-Weighted Preference Optimization for Neural Combinatorial Optimization
Aug 12, 2026Yuanyu Li, Jintao Xu, Zijiang Liu +6Combinatorial Optimization Preference Optimization
Supply Chain Tech Team Y, JD.com, Beijing, China · Department of Industrial Engineering, Tsinghua University, Beijing, China · Faculty of Engineering and Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China
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
ReCo: Reweighting GRPO Against Distributional Concentration
Jul 29, 2026Junoh Park, Junseo Hwang, Wonguk Cho +1Reasoning Diversity RL for Language Model Reasoning
Graduate School of Data Science, Seoul National University
Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Jul 16, 2026Joshua Spear, Matthieu Komorowski, Rebecca Pope +2Off-Policy Evaluation Importance Sampling
Institute of Child Health, University College London · Strive Health Ltd. · National Institute for Health Research +3
Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Jul 7, 2026Kisung YouImportance Sampling Importance Weighting
Baruch College
On the Position Bias of On-Policy Distillation
Jun 21, 2026Yan Xie, Sijie Zhu, Tiansheng Wen +2Importance Weighting Language Model Distillation
Xidian University · Georgia Institute of Technology · Amazon AGI SF Lab
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
May 29, 2026Salim I. Amoukou, Emanuele Albini, Tom Bewley +2Distribution Shift Robustness Unsupervised Domain Adaptation
J.P. Morgan AI Research
Quotient DAGs for Off-Policy Evaluation:Forward-Flow Importance Sampling and Exact Slate Propensities
May 28, 2026Ziwen Xie, Shaowen Xiang, Hongyu He +1Off-Policy Evaluation Importance Sampling
Shanghai Jiao Tong University · National University of Singapore
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators
May 25, 2026Tongtong Fang, Nan Lu, Gang Niu +2Importance Weighting
The Institute of Statistical Mathematics, Tokyo, Japan. · University of Bristol, Bristol, U.K. · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. +1
Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
May 17, 2026Paulo Akira F. Enabe, Rodrigo ProvasiPAC-Bayesian Generalization Bounds Domain Adaptation
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. WainwrightUnsupervised Domain Adaptation Distribution Shift
Lab for Information and Decision Systems · Statistics and Data Science Center · EECS† +2
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
May 9, 2026Antong Zhang, Han Qi, Heng YangSynthetic-to-Real Domain Adaptation Robot Policy Learning
Department of Computer Science, Brown University · School of Engineering and Applied Sciences, Harvard University
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Apr 22, 2026Alexander Weers, Daniel Rueckert, Martin J. MentenMedical Report Generation Healthcare
TUM School of Computation, Information and Technology, Technical University of Munich, DE · Munich Center for Machine Learning (MCML), DE · Department of Computing, Imperial College London, UK
Feature weighting for data analysis via evolutionary simulation
Nov 9, 2025Aris Daniilidis, Alberto Domínguez Corella, Philipp WissgottReplicator Dynamics Importance Weighting
Institut für Stochastik und Wirtschaftsmathematik, Variational Analysis, Dynamics and Operations Research Unit E105-04, Technische Universität Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria · Institut für Mathematik und Wissenschaftliches Rechnen, Universität Graz, Heinrichstraße 36, A-8010 Graz, Austria · danube.ai solutions gmbh, 1040 Vienna, Austria

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From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift

Mushan Li, Kihyun Han, Yanyuan Ma

Department of Statistics, The Pennsylvania State University University Park, PA 16802, USA

Label Shift Confidence Region Estimation Importance Weighting

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Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty associated with their estimation. Existing methods typically analyze this uncertainty through Gaussian elimination on interval-valued linear systems, which leads to overly conservative confidence regions and inefficient downstream applications. We propose a paradigm shift from inversion-based inference to a direct matrix constraint framework. We use this framework to define a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity. Furthermore, we analyze the confidence region's geometry and provide the theoretical results for its diameter bounds. Evaluated across text, image, multimodal benchmarks, including AGNews, MNIST, CIFAR-10, N24News, and a real-world autonomous driving dataset, nuImages, our approach consistently yields shorter confidence intervals and smaller prediction sets than inversion-based methods.

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