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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Bayesian Adaptation Gym: A Benchmark for the Bayesian Low-Rank Adaptation of Multi-Modal Language Models
Jun 20, 2026Colin Samplawski, Ramneet Kaur, Manoj Acharya +2Distribution Shift Robustness Multimodal Large Language Models
Neuro-Symbolic Computing and Intelligence Research Group, Computer Science Laboratory, SRI International
Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics
Jun 20, 2026Chakshu BawejaDistribution Shift Robustness
Ashforde OÜ
Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
Jun 20, 2026Ignacio Cabrera Martin, Marcello Trovati, Almas Baimagambetov +1Distribution Shift Robustness Concept Drift
University of Brighton, School of Architecture, Technology and Engineering, Brighton, BN2 4GJ, United Kingdom · University of Lancashire, School of Business, Preston, PR1 2HE, United Kingdom
Toward Calibrated Mixture-of-Experts Under Distribution Shift
Jun 18, 2026Gina Wong, Drew Prinster, Suchi Saria +2Distribution Shift Robustness Model Calibration
Johns Hopkins University, Baltimore, MD, USA.
Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems
Jun 17, 2026Bernardo Feijó Junqueira, Claudio Kiyoshi Umezu, Bruno Bilhar Karaziack +2Distribution Shift Robustness Domain Adaptation
Department of Mechanical Engineering, Rio de Janeiro State University (UERJ), Fonseca Teles Street, 121, Rio de Janeiro, 20940-200, RJ, Brazil. · Faculty of Agricultural Engineering, University of Campinas (UNICAMP), Av. Cˆandido Rondon, 501, Campinas, 13083-875, SP, Brazil. · Directorate of Technology and Innovation, CPQD, R. Dr. Ricardo Benetton Martins, 1000 - Parque II, Campinas, 13086-902, SP, Brazil. +2
Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption
Jun 17, 2026Navyansh Mahla, Prateek Chanda, Ganesh RamakrishnanDistribution Shift Robustness Probabilistic Forecasting
Indian Institute of Technology, Bombay
Robustness of Similarity-based Positional Encoding Under Rotations: Theoretical Analysis and Experimental Validation
Jun 16, 2026Andrea Santomauro, Luigi Portinale, Giorgio LeonardiDistribution Shift Robustness Positional Encoding
Computer Science Institute, DiSIT, University of Piemonte Orientale, Alessandria, Italy
A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning
Jun 15, 2026Ardianto Wibowo, Paulo E Santos, Amer Baghdadi +3Distribution Shift Robustness Non-Stationary RL
IMT Atlantique, Brest, France · Flinders University, Adelaide, Australia · IRL Crossing, Adelaide, Australia +1
Robust Conformal CBF and CLF Controllers via Iterative Policy Updates
Jun 13, 2026Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni +1Distribution Shift Robustness Lyapunov-Based Control
1Automatic Control Laboratory, ETH Zürich, Switzerland. · 2Computer and Information Science, University of Pennsylvania, USA. · 3Electrical and Systems Engineering, University of Pennsylvania, USA.
Audited Conformal Prediction for Classification under Unknown Distribution Shift
Jun 12, 2026Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1Distribution Shift Robustness Uncertainty Quantification
Department of Data Sciences and Operations University of Southern California Los Angeles, CA 90089 · AI Foundations, Capital One New York, NY 10017 · Department of Data Sciences and Operations Thomas Lord Department of Computer Science University of Southern California Los Angeles, CA 90089
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Jun 12, 2026Annie Ulichney, Amanda CostonDouble ML Distribution Shift Robustness
Department of Statistics, University of California, Berkeley
Neural Variability Enhances Artificial Network Robustness
Jun 11, 2026Robin Preble, Praveen Venkatesh, Stefan Mihalas +1Distribution Shift Robustness Neural Network Robustness
Department of Computer Science, Western Washington University, Bellingham, WA 98225 · Allen Institute, Seattle, WA 98195
TEDD: Robust Detection of Unstable Temporal Features
Jun 10, 2026Ricardo Ribeiro Pereira, Bruno Casal Laraña, Nádia Soares +1Distribution Shift Robustness Distribution Shift Detection
DCC/FCUP University of Porto Porto, Portugal · Feedzai Porto, Portugal · Mindera Porto, Portugal
Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots
Jun 10, 2026Zhi Wei Xu, Torbjörn E. M. NordlingRemote Photoplethysmography Distribution Shift Robustness
Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan.
Policy-driven Conformal Prediction for Trustworthy QoT Estimation
Jun 10, 2026Kiarash Rezaei, Omran Ayoub, Paolo Monti +1Distribution Shift Robustness Conformal Prediction
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden · University of Applied Sciences and Arts of Southern Switzerland, 6928 Lugano, Switzerland
Generalized Conformal Predictive Systems Under Distributional Shifts
Jun 9, 2026Jef Jonkers, Johanna ZiegelDistribution Shift Robustness Weighted Conformal Prediction
IDLab Department of Electronics and Information Systems Ghent University Ghent, Belgium · Seminar for Statistics ETH Zurich Zurich, Switzerland
Bridging Domain Expertise and Generalization for Performance Estimation
Jun 4, 2026Shuxuan Li, Zhilin Zhao, Quyu Kong +1Distribution Shift Robustness
School of Computer Science and Engineering, Sun Yat-sen University, China · 2Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China · 3Shenzhen Loop Area Institute, China +1
CHALIS: A Challenge Dataset for Language Identification in Difficult Scenarios
Jun 4, 2026Michal Tichý, Jindřich LibovickýDistribution Shift Robustness Language Identification
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, V Holešovičkách 747/2, 180 00 Praha, Czechia
ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models
Jun 2, 2026Sotirios Vavaroutas, Yu Yvonne Wu, Ali Etemad +1Distribution Shift Robustness Unsupervised Domain Adaptation
University of Cambridge, UK · Dartmouth College, USA · Queen’s University, Canada
Low-Frequency Shortcuts in Texture-Driven Visual Learning
Jun 2, 2026Utku Şirin, Cathy Hou, David Alvarez-Melis +1Distribution Shift Robustness Frequency-Domain Feature Learning
Harvard University
Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment
Jun 1, 2026Yiran Qiao, Jing Chen, Jiaqi Xu +3Distribution Shift Robustness Causal Representation Learning
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · ByteDance China, Hangzhou, China · State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences +1
Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging
Jun 1, 2026Tim Nielen, Sameer Ambekar, Johannes Kiechle +2Distribution Shift Robustness Test-Time Adaptation
School of Computation, Information and Technology, Technical University of Munich, Germany · Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany · Munich Center for Machine Learning (MCML) +3
Regime-Arrival Uncertainty in Generalization Bounds under Distribution Shift
Jun 1, 2026Prince PoudelDistribution Shift Robustness Statistical Learning Theory
Independent researcher.
DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
May 30, 2026Enver Menadjiev, Jihyeon Seong, Jisu Yeo +1Distribution Shift Robustness Conformal Prediction
Korea Advanced Institute of Science and Technology, South Korea · INEEJI, South Korea
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
From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
May 29, 2026Firas Gabetni, Alexandre Rocchi Henry, Nacim Belkhir +2Distribution Shift Robustness Covariate Shift
U2IS, ENSTA · Institut Polytechnique de Paris · AMIAD, Pôle Recherche, Palaiseau
TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness
May 28, 2026Michał Kozyra, Gesine ReinertDistribution Shift Robustness Neural Network Robustness
Department of Statistics, University of Oxford, United Kingdom
Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
May 28, 2026Yankai Chen, Hanrong Zhang, Bowei He +2Distribution Shift Robustness Representation Learning
MBZUAI · McGill University · University of Illinois Chicago
Positional Encodings Anchor Spatial Structure in Vision Transformers: A Geometric Perspective on Robustness
May 28, 2026Mahmoud MannesDistribution Shift Robustness Vision Transformer
ESSTHS
How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environments
May 28, 2026Ji-Hoon Hwang, Daeyoung Kim, Hyung-Suk Yoon +2Distribution Shift Robustness Domain Generalization
Department of Electrical and Communication Engineering, Seoul National University, Korea

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Bayesian Adaptation Gym: A Benchmark for the Bayesian Low-Rank Adaptation of Multi-Modal Language Models

Colin Samplawski, Ramneet Kaur, Manoj Acharya, Anirban Roy+1

Neuro-Symbolic Computing and Intelligence Research Group, Computer Science Laboratory, SRI International

Distribution Shift Robustness Multimodal Large Language Models Low-Rank Adaptation Language Model Calibration Multimodal Model Evaluation Bayesian LoRA

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Large multi-modal language models are increasingly deployed in high-stakes domains, making well-calibrated uncertainty essential. Traditional Bayesian methods approximate posteriors over all model weights, which becomes intractable for modern large models. For this reason, recent work instead considers Bayesian low-rank adaptation to enable tractable posterior approximation. Due to a lack of a standardized benchmark to evaluate these approaches, it remains unclear where these methods provide meaningful benefits. To fill this gap, we introduce Bayesian Adaptation Gym (BAG), a benchmark for the Bayesian adaptation of multi-modal language models. BAG provides reference implementations of classic Bayesian baselines and state-of-the-art adaptation methods, along with a multi-modal dataset and task suite designed to probe calibration, robustness under distribution shift, and decision-making under uncertainty via active learning. Using BAG, we conduct and report extensive experiments across model sizes, datasets, and tasks to highlight the successes and failures of current Bayesian adaptation approaches. To enable further research, BAG is fully open source: https://github.com/SRI-CSL/BayesAdapt.

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