Relevant bibliographies by topics / Out-of-distribution generalization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Out-of-distribution generalization'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Out-of-distribution generalization"

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Ye, Nanyang, Lin Zhu, Jia Wang, et al. "Certifiable Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.

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Machine learning methods suffer from test-time performance degeneration when faced with out-of-distribution (OoD) data whose distribution is not necessarily the same as training data distribution. Although a plethora of algorithms have been proposed to mitigate this issue, it has been demonstrated that achieving better performance than ERM simultaneously on different types of distributional shift datasets is challenging for existing approaches. Besides, it is unknown how and to what extent these methods work on any OoD datum without theoretical guarantees. In this paper, we propose a certifiab
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Liu, Bowen, Haoyang Li, Shuning Wang, Shuo Nie, and Shanghang Zhang. "Subgraph Aggregation for Out-of-Distribution Generalization on Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18763–71. https://doi.org/10.1609/aaai.v39i18.34065.

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Out-of-distribution (OOD) generalization in Graph Neural Networks (GNNs) has gained significant attention due to its critical importance in graph-based predictions in real-world scenarios. Existing methods primarily focus on extracting a single causal subgraph from the input graph to achieve generalizable predictions. However, relying on a single subgraph can lead to susceptibility to spurious correlations and is insufficient for learning invariant patterns behind graph data. Moreover, in many real-world applications, such as molecular property prediction, multiple critical subgraphs may influ
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Yuan, Lingxiao, Harold S. Park, and Emma Lejeune. "Towards out of distribution generalization for problems in mechanics." Computer Methods in Applied Mechanics and Engineering 400 (October 2022): 115569. http://dx.doi.org/10.1016/j.cma.2022.115569.

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Liu, Anji, Hongming Xu, Guy Van den Broeck, and Yitao Liang. "Out-of-Distribution Generalization by Neural-Symbolic Joint Training." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12252–59. http://dx.doi.org/10.1609/aaai.v37i10.26444.

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This paper develops a novel methodology to simultaneously learn a neural network and extract generalized logic rules. Different from prior neural-symbolic methods that require background knowledge and candidate logical rules to be provided, we aim to induce task semantics with minimal priors. This is achieved by a two-step learning framework that iterates between optimizing neural predictions of task labels and searching for a more accurate representation of the hidden task semantics. Notably, supervision works in both directions: (partially) induced task semantics guide the learning of the ne
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Yu, Yemin, Luotian Yuan, Ying Wei, et al. "RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 374–82. http://dx.doi.org/10.1609/aaai.v38i1.27791.

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Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions. Despite steady progress on standard benchmarks, our understanding of existing retrosynthesis prediction models under the premise of distribution shifts remains stagnant. To this end, we first formally sort out two types of distribution shifts in retrosynthesis prediction and construct two groups of benchmark datasets. Next, through comprehe
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Du, Hongyi, Xuewei Li, and Minglai Shao. "Graph out-of-distribution generalization through contrastive learning paradigm." Knowledge-Based Systems 315 (April 2025): 113316. https://doi.org/10.1016/j.knosys.2025.113316.

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Xu, Yiming, Bin Shi, Zhen Peng, Huixiang Liu, Bo Dong, and Chen Chen. "Out-of-Distribution Generalization on Graphs via Progressive Inference." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12963–71. https://doi.org/10.1609/aaai.v39i12.33414.

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The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap betwee
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Zhu, Lin, Xinbing Wang, Chenghu Zhou, and Nanyang Ye. "Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11461–69. http://dx.doi.org/10.1609/aaai.v37i9.26355.

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Recent advances in large pre-trained models showed promising results in few-shot learning. However, their generalization ability on two-dimensional Out-of-Distribution (OoD) data, i.e., correlation shift and diversity shift, has not been thoroughly investigated. Researches have shown that even with a significant amount of training data, few methods can achieve better performance than the standard empirical risk minimization method (ERM) in OoD generalization. This few-shot OoD generalization dilemma emerges as a challenging direction in deep neural network generalization research, where the pe
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Lavda, Frantzeska, and Alexandros Kalousis. "Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation." Entropy 25, no. 12 (2023): 1659. http://dx.doi.org/10.3390/e25121659.

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Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast, generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundamental challenges for machine learning models. Moreover, obtaining high-quality labeled examples can be very time-consuming and expensive, particularly when specialized skills are required for labeling. To address these issues, we propose BtVAE, a method that utilizes conditional VAE models to achieve combinatorial generalization in certain scenario
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Zhang, Xiao, Sunhao Dai, Jun Xu, Yong Liu, and Zhenhua Dong. "AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22596–604. https://doi.org/10.1609/aaai.v39i21.34418.

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Online to batch conversion involves constructing a new batch learner by utilizing a series of models generated by an existing online learning algorithm, for achieving generalization guarantees under i.i.d assumption. However, when applied to real-world streaming applications such as streaming recommender systems, the data stream may be sampled from time-varying distributions instead of persistently being i.i.d. This poses a challenge in terms of out-of-distribution (OOD) generalization. Existing approaches employ fixed conversion mechanisms that are unable to adapt to novel testing distributio
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Dissertations / Theses on the topic "Out-of-distribution generalization"

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Kirchmeyer, Matthieu. "Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS080.

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L’apprentissage profond a émergé comme une approche puissante pour la modélisation de données statiques comme les images et, plus récemment, pour la modélisation de systèmes dynamiques comme ceux sous-jacents aux séries temporelles, aux vidéos ou aux phénomènes physiques. Cependant, les réseaux neuronaux ne généralisent pas bien en dehors de la distribution d’apprentissage, en d’autres termes, hors-distribution. Ceci limite le déploiement de l’apprentissage profond dans les systèmes autonomes ou les systèmes de production en ligne, qui sont confrontés à des données en constante évolution. Dans
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Soum-Fontez, Louis. "LiDAR-based domain generalization and unknown 3D object detection." Electronic Thesis or Diss., Université Paris sciences et lettres, 2025. http://www.theses.fr/2025UPSLM002.

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Le développement de systèmes de perception robustes est fondamental pour assurer le fonctionnement sûr et efficace des véhicules autonomes. Ces systèmes accomplissent des tâches essentielles telles que la détection d'objets en 3D, permettant aux véhicules d'identifier et de localiser des obstacles, notamment d'autres véhicules, des piétons et divers objets présents dans leur environnement. Une détection précise est cruciale pour une prise de décision efficace et une navigation sûre dans des scénarios de conduite complexes. Cependant, la détection d'objets en 3D pose des défis importants en rai
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Books on the topic "Out-of-distribution generalization"

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Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theor
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James, Philip. The Biology of Urban Environments. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198827238.001.0001.

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Urban environments are characterized by the density of buildings and elements of a number of infrastructures that support urban residents in their daily life. These built elements and the activities that take place within towns and cities create a distinctive climate and increase air, water, and soil pollution. Within this context the elements of the natural environment that either are residual areas representative of the pre-urbanized area or are created by people contain distinctive floral and faunal communities that do not exist in the wild. The diverse prions, viruses, micro-organisms, pla
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Book chapters on the topic "Out-of-distribution generalization"

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Chen, Zining, Weiqiu Wang, Zhicheng Zhao, Aidong Men, and Hong Chen. "Bag of Tricks for Out-of-Distribution Generalization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_31.

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Moruzzi, Caterina. "Toward Out-of-Distribution Generalization Through Inductive Biases." In Studies in Applied Philosophy, Epistemology and Rational Ethics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09153-7_5.

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Li, Dongqi, Zhu Teng, Qirui Li, and Ziyin Wang. "Sharpness-Aware Minimization for Out-of-Distribution Generalization." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_43.

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Wang, Fawu, Kang Zhang, Zhengyu Liu, Xia Yuan, and Chunxia Zhao. "Deep Relevant Feature Focusing for Out-of-Distribution Generalization." In Pattern Recognition and Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_19.

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Nguyen, Bac, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, and Aaron Courville. "SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72890-7_9.

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Teevno, Mansoor Ali, Gilberto Ochoa-Ruiz, and Sharib Ali. "Tackling Domain Generalization for Out-of-Distribution Endoscopic Imaging." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73290-4_5.

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Jung, Yoon Gyo, Jaewoo Park, Xingbo Dong, Hojin Park, Andrew Beng Jin Teoh, and Octavia Camps. "Face Reconstruction Transfer Attack as Out-of-Distribution Generalization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73226-3_23.

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Wang, Yuqing, Xiangxian Li, Zhuang Qi, et al. "Meta-Causal Feature Learning for Out-of-Distribution Generalization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_36.

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Yu, Haoran, Baodi Liu, Yingjie Wang, Kai Zhang, Dapeng Tao, and Weifeng Liu. "A Stable Vision Transformer for Out-of-Distribution Generalization." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_27.

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Zhang, Xingxuan, Yue He, Tan Wang, et al. "NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_29.

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Conference papers on the topic "Out-of-distribution generalization"

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Cho, Daniel, Christopher Ebersole, and Edmund Zelnio. "Predictive measures of out-of-distribution generalization." In Algorithms for Synthetic Aperture Radar Imagery XXXII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2025. https://doi.org/10.1117/12.3054360.

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Wang, Song, Xiaodong Yang, Rashidul Islam, et al. "Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00108.

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Jin, Kaiyu, Chenwang Wu, and Defu Lian. "Out-of-Distribution Generalization via Style and Spuriousness Eliminating." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687911.

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Liu, Wenliang, Guanding Yu, Lele Wang, and Renjie Liao. "An Information-Theoretic Framework for Out-of-Distribution Generalization." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619471.

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Chen, Zining, Weiqiu Wang, Zhicheng Zhao, Fei Su, and Aidong Men. "Selective Cross-Correlation Consistency Loss for Out-of-Distribution Generalization." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10688222.

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Chowdhury, Jawad, and Gabriel Terejanu. "CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013260400003905.

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Zhang, Min, Zifeng Zhuang, Zhitao Wang, and Donglin Wang. "RotoGBML: Towards Out-of-distribution Generalization for Gradient-based Meta-learning." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687395.

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Qiao, Yi, Yang Liu, Qing He, and Xiang Ao. "Domain-aware Node Representation Learning for Graph Out-of-Distribution Generalization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889630.

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Qi, Zhuang, Weihao He, Xiangxu Meng, and Lei Meng. "Attentive Modeling and Distillation for Out-of-Distribution Generalization of Federated Learning." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687423.

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Petchhan, Jirayu, Muhammad Firdaus Alhakim, and Shun-Feng Su. "Out-of-Distribution Awareness via Domain Generalization for Industrial Production Surveillance Employment." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674660.

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Reports on the topic "Out-of-distribution generalization"

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Matthias, Caro, Huang Hsin-Yuan, Cincio Lukasz, et al. Out-of-Distribution Generalization for Learning Quantum Dynamics. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/2377336.

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