Distribution Shift Robustness

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26 papers in the last four weeks, up 225% on the four weeks before. 0.3% of all new papers.

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EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
May 28, 2026Kelsey Doerksen, Hannah KernerDistribution Shift Robustness Geospatial Foundation Models
School of Computing and Augmented Intelligence Arizona State University
Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
May 28, 2026Mengdi Chu, Yang Liu, Ayan Biswas +1Distribution Shift Robustness Neural Network Generalization
The Ohio State University · Los Alamos National Laboratory
Unification and Optimization of Robust Supervised Learning
May 27, 2026Jonas Hanselle, Valentin Margraf, Clemens Damke +1Distribution Shift Robustness Robust Optimization
LMU Munich, MCML · LMU Munich, MCML, DFKI
Conformalised imprecise inference for robust extrapolation under limited data
May 25, 2026Yu Chen, Scott FersonDistribution Shift Robustness Uncertainty Quantification
Institute for Risk and Uncertainty · University of Liverpool · Liverpool
Mean-Shift PCA by Knockoff Mean
May 25, 2026Mengda Li, Zeng Li, Jianfeng YaoDistribution Shift Robustness Principal Component Analysis
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
May 25, 2026Hyung-Suk Yoon, Seung-Woo SeoDistribution Shift Robustness Imitation Learning
Department of Electronic and Computer Engineering, Seoul National University, Seoul, South Korea.
Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation
May 24, 2026Xi Ding, Lei Wang, Syuan-Hao Li +1Distribution Shift Robustness Unsupervised Domain Adaptation
School of Engineering and Built Environment, Griffith University, Australia
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
May 22, 2026Ahmed Sabbah, Mohammed Kharma, Radi Jarrar +2Distribution Shift Robustness Concept Drift
aDepartment of Computer Science, Birzeit University, P.O. Box 14, Birzeit, P627, West2026 Bank, Palestine · bDepartment of Computer Science, University of Central Florida, 4328 Scorpius St., Building 116, Room 246, Orlando, 32816, FL, USAMay 22
Convex Low-resource Accent-Robust Language Detection in Speech Recognition
May 22, 2026Miria Feng, William Tan, Mert PilanciDistribution Shift Robustness Speech Processing
Department of Electrical Engineering, Stanford University, California, United States. · Department of Computer Science, Stanford University, California, United States.
Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions
May 22, 2026Yuefeng Liu, Ning Yang, Ziyu YangDistribution Shift Robustness Time Series Imputation
School of Digital and Intelligent Industry (School of Cyber Science and Technology), Inner Mongolia University of Science and Technology, Baotou, China
The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning
May 21, 2026Vishal RajputDistribution Shift Robustness Jacobian Regularization
KU Leuven
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
May 21, 2026Bradley Stanley-Clamp, Anson Lei, Hannah M. Christensen +1Distribution Shift Robustness Climate Science
Applied AI Lab · University of Oxford, UK · Atmospheric, Oceanic and Planetary Physics
Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
May 21, 2026Christo Kurisummoottil Thomas, Emilio Calvanese StrinatiDistribution Shift Robustness Wireless Communications
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA · CEA-Leti, Grenoble, France
Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
May 21, 2026Sungjun Lim, Heedong Kim, Andrew Lee +1Distribution Shift Robustness OOD Generalization
aYonsei University · bHarvard University
MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation
May 20, 2026Ahanaf Hasan AriqDistribution Shift Robustness PAC-Bayesian Generalization Bounds
Ideal School and College
A New Framework to Analyse the Distributional Robustness of Deep Neural Networks
May 20, 2026Divij Khaitan, Subhashis BanerjeeDistribution Shift Robustness Neural Network Generalization
Microsoft · Ashoka University
RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
May 19, 2026Shuoqin Zhang, Yixin Xiong, Xiru Gao +4Distribution Shift Robustness Robot Skill Learning
Chongqing University · Chengdu Anu Intelligence
FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes
May 19, 2026Anya Fries, Jacob A Nelson, Martin Jung +2Distribution Shift Robustness OOD Generalization
Seminar for Statistics, ETH Zürich Zürich, Switzerland · Max Planck Institute for Biogeochemistry Jena, Germany
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
May 19, 2026Kieran Wood, Stefan Zohren, Stephen J. RobertsDistribution Shift Robustness Time Series Forecasting
Machine Learning Research Group, University of Oxford · Oxford-Man Institute, University of Oxford
Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +2Distribution Shift Robustness Algorithmic Bias
Australian Institute for Machine Learning, Adelaide University, Adelaide, Australia
Conformal Prediction via Transported Beta Laws
May 18, 2026Thiago R. Ramos, Helton Graziadei, Luben M. C. CabezasDistribution Shift Robustness Conformal Prediction
Federal University of S˜ao Carlos · Federal University of S˜ao Carlos, University of S˜ao Paulo, Inria and Universit´e Grenoble Alpes
Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
May 18, 2026Mohamed Bouadi, Nassim Bouarour, Varun Kulkarni +3Synthetic Data Pretraining Distribution Shift Robustness
Lexsi Labs
Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
May 18, 2026Zhilin Zhu, Yabin Wang, Zhiheng Ma +3Distribution Shift Robustness Test-Time Adaptation
Harbin Institute of Technology · Pengcheng Laboratory · Shenzhen University of Advanced Technology +1
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
May 18, 2026Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston +1Distribution Shift Robustness Post-Hoc Calibration
AI4Health Department, University of Oldenburg, Oldenburg, Germany · Department of Data Science and AI, National Physical Laboratory, Teddington, United Kingdom · School of Mathematics and Physics, University of Surrey, Surrey, United Kingdom
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
May 17, 2026Tongze Wang, Xiaohui Xie, Wenduo Wang +2Distribution Shift Robustness Domain Adaptation
Institute for Network Sciences and Cyberspace, Tsinghua University · Department of Computer Science and Technology, Tsinghua University
Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty
May 17, 2026Ren Ozeki, Hamada Rizk, Hirozumi YamaguchiDistribution Shift Robustness Contrastive Learning
Osaka University, Suita, Japan · Japan Meteorological Agency · RIKEN Center for Computational Science, Kobe, Japan +1
Iterative Chow Filtering for Learning with Distribution Shift
May 17, 2026Gautam Chandrasekaran, Georgios Gkrinias, Adam R. Klivans +2Distribution Shift Robustness Boolean Function Learning
UT Austin
Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
May 15, 2026Jinhao Zhang, Kangfei Zhao, Qiuhao Zeng +1Distribution Shift Robustness Temporal GNNs
Beijing Institute of Technology · University of Toronto · Hong Kong Baptist University
Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models
May 15, 2026Fabian Morelli, Arnas Uselis, Ankit Sonthalia +1Distribution Shift Robustness VLM Robustness
University of Tübingen ∗ · University of Tübingen
JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
May 13, 2026Lavsen Dahal, Yubraj Bhandari, Geoffrey Rubin +1Distribution Shift Robustness Healthcare
Center for Virtual Imaging Trials, RAI Labs, Department of Radiology, Duke University, Durham NC 27708, USA · Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA · Department of Mathematics, Trinity College of Arts & Sciences, Duke University,2026 Durham, NC 27708, USA +1

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EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation

Kelsey Doerksen, Hannah Kerner

School of Computing and Augmented Intelligence Arizona State University

Distribution Shift Robustness Geospatial Foundation Models OOD Generalization Remote Sensing Remote Sensing Benchmarks

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Current Earth observation benchmarks focus on measuring performance on diverse tasks and applications, typically measuring generalization in-distribution. But when models are deployed, they must generalize to myriad out-of-distribution scenarios, such as new time periods, geographies, scales, and sensors. We introduce EarthShift: the first public testbed for benchmarking robustness across multiple realistic distribution shifts encountered in remote sensing. EarthShift enables users to measure distributional robustness by comparing performance in- and out-of-distribution using datasets from paired datasets from different sources, temporal windows, geographic locations, and sensors. Our experiments on 8 geospatial foundation models (GFMs) and 11 tasks covering 5 shift types show that GFMs consistently perform 15-20% worse out-of-distribution on average regardless of model architecture, size, pre-training or fine-tuning strategy. We show that GFM robustness is similar to that of generic vision foundation models, and even fully-supervised models. This highlights a need for future research to strive for improvements in distributional robustness, not just performance, which can be benchmarked using EarthShift. We release our code and datasets to provide a testbed to guide future work to create foundation models that are robust and reliable in real-world applications. Code and data for EarthShift are available at: https://earthshift.github.io

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