Out of Distribution Generalization in Machine Learning

3 March 2021

Papers citing "Out of Distribution Generalization in Machine Learning"

50 / 51 papers shown

Title
When can in-context learning generalize out of task distribution? Chase Goddard Lindsay M. Smith Vudtiwat Ngampruetikorn David J. Schwab OOD 30 0 0 05 Jun 2025
Weight-Space Linear Recurrent Neural Networks Roussel Desmond Nzoyem Nawid Keshtmand Idriss Tsayem David A.W. Barton Tom Deakin 36 0 0 01 Jun 2025
CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning Ke Niu Z. Chen Haiyang Yu Yuwen Chen Teng Fu Mengyang Zhao Bin Li Xiangyang Xue 25 0 0 31 May 2025
Robust Invariant Representation Learning by Distribution Extrapolation Kotaro Yoshida Konstantinos Slavakis OOD 99 0 0 22 May 2025
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Yiyuan Yang Guodong Long Dinesh Manocha Qinghua Lu Shanshan Ye Jing Jiang OODD 510 2 0 02 May 2025
Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning Huilin Deng Ding Zou Rui Ma Hongchen Luo Yang Cao Yu Kang LRM VLM 115 22 0 10 Mar 2025
Prospective Learning: Learning for a Dynamic Future Ashwin De Silva Rahul Ramesh Rubing Yang Siyu Yu Joshua T. Vogelstein Pratik Chaudhari AI4TS 170 1 0 31 Oct 2024
NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework Shuangchen Zhao Changde Du Hui Li Huiguang He 72 0 0 27 Aug 2024
Disentangled Representations for Causal Cognition Filippo Torresan Manuel Baltieri CML 101 2 0 30 Jun 2024
Domain Generalisation via Imprecise Learning Anurag Singh Siu Lun Chau S. Bouabid Krikamol Muandet AI4CE OOD 100 10 0 06 Apr 2024
Dual-Personalizing Adapter for Federated Foundation Models Yiyuan Yang Guodong Long Taoshu Shen Jing Jiang Michael Blumenstein 128 27 0 28 Mar 2024
Generalising realisability in statistical learning theory under epistemic uncertainty Fabio Cuzzolin CML 83 0 0 22 Feb 2024
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration Kotaro Yoshida Hiroki Naganuma 155 2 0 31 Jan 2024
Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models Beier Zhu Kaihua Tang Qianru Sun Hanwang Zhang 78 22 0 12 Oct 2023
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification P. Melki Lionel Bombrun Boubacar Diallo Jérôme Dias Jean-Pierre da Costa 184 7 0 29 Aug 2023
A Causal Framework to Unify Common Domain Generalization Approaches N. Zhang Kaican Li Han Gao Weiyan Xie Zhi Lin Zechao Li Luning Wang Yongxiang Huang FedML OOD AI4CE 83 3 0 13 Jul 2023
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL Miguel Suau M. Spaan F. Oliehoek CML 90 6 0 04 Jun 2023
Global Layers: Non-IID Tabular Federated Learning Yazan Obeidi FedML 99 0 0 29 May 2023
Consistency Regularization for Domain Generalization with Logit Attribution Matching Han Gao Kaican Li Weiyan Xie Zhi Lin Yongxiang Huang Luning Wang Caleb Chen Cao N. Zhang 73 2 0 13 May 2023
Masked Images Are Counterfactual Samples for Robust Fine-tuning Yao Xiao Ziyi Tang Pengxu Wei Cong Liu Liang Lin 131 19 0 06 Mar 2023
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance Sotiris Pelekis Evangelos Karakolis Francisco Silva Vasileios Schoinas S. Mouzakitis Georgios Kormpakis N. Amaro J. Psarras AI4TS 62 15 0 25 Feb 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play J. Liu Krishnamurthy Dvijotham Jihyeon Janel Lee Quan Yuan Martin Strobel Balaji Lakshminarayanan Deepak Ramachandran 75 5 0 11 Feb 2023
Subset verification and search algorithms for causal DAGs Davin Choo Kirankumar Shiragur CML 56 11 0 09 Jan 2023
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL Chen Sun Wannan Yang Thomas Jiralerspong Dane Malenfant Benjamin Alsbury-Nealy Yoshua Bengio Blake A. Richards OffRL 49 2 0 12 Oct 2022
Meta-Causal Feature Learning for Out-of-Distribution Generalization Yuqing Wang Xiangxian Li Zhuang Qi Jingyu Li Xuelong Li Xiangxu Meng Lei Meng OOD OODD BDL 100 25 0 22 Aug 2022
Invariant Feature Learning for Generalized Long-Tailed Classification Kaihua Tang Mingyuan Tao Jiaxin Qi Zhenguang Liu Hanwang Zhang VLM 96 56 0 19 Jul 2022
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms Runpeng Yu Hong Zhu Kaican Li Lanqing Hong Rui Zhang Nan Ye Shao-Lun Huang Xiuqiang He 65 6 0 12 Jun 2022
(Im)possibility of Collective Intelligence Krikamol Muandet 261 6 0 05 Jun 2022
Out-of-distribution generalization for learning quantum dynamics Matthias C. Caro Hsin-Yuan Huang Nic Ezzell Joe Gibbs A. Sornborger L. Cincio Patrick J. Coles Zoë Holmes OODD OOD 106 88 0 21 Apr 2022
Dimensionless machine learning: Imposing exact units equivariance Soledad Villar Weichi Yao D. Hogg Ben Blum-Smith Bianca Dumitrascu 62 28 0 02 Apr 2022
Towards understanding deep learning with the natural clustering prior Simon Carbonnelle 52 0 0 15 Mar 2022
Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs Jyoti Narwariya Chetan Verma Pankaj Malhotra Lovekesh Vig E. Subramanian Sanjay Bhat AI4TS 71 3 0 11 Feb 2022
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems Miguel Suau Jinke He M. Spaan F. Oliehoek 58 4 0 03 Feb 2022
Gradient Masked Averaging for Federated Learning Irene Tenison Sai Aravind Sreeramadas Vaikkunth Mugunthan Edouard Oyallon Irina Rish Eugene Belilovsky FedML 121 25 0 28 Jan 2022
Prospective Learning: Principled Extrapolation to the Future Ashwin De Silva Rahul Ramesh Pallavi V. Kulkarni M. Shuler Noah J. Cowan ... Andrei A. Rusu Timothy D. Verstynen Konrad Paul Kording Pratik Chaudhari Joshua T. Vogelstein AI4TS 94 4 0 19 Jan 2022
Provable Adversarial Robustness in the Quantum Model Khashayar Barooti Grzegorz Gluch R. Urbanke AAML OOD 42 1 0 17 Dec 2021
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology Tian Cai Li Xie Muge Chen Yang Liu Di He Shuo-feng Zhang C. Mura P. Bourne Lei Xie OOD 69 4 0 23 Nov 2021
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers Gabriele Prato Simon Guiroy Ethan Caballero Irina Rish Sarath Chandar VLM 91 12 0 13 Oct 2021
Towards a theory of out-of-distribution learning Jayanta Dey Ali Geisa Ronak D. Mehta Tyler M. Tomita Hayden S. Helm Haoyin Xu Eric Eaton Jeffery Dick Carey E. Priebe Joshua T. Vogelstein OOD 99 21 0 29 Sep 2021
Optimization-based Causal Estimation from Heterogenous Environments Mingzhang Yin Yixin Wang David M. Blei OOD 134 18 0 24 Sep 2021
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts Masanari Kimura Takuma Nakamura Yuki Saito OOD 96 3 0 30 Aug 2021
A topological solution to object segmentation and tracking Thomas R. Tsao Doris Y. Tsao VOS 43 17 0 05 Jul 2021
Dangers of Bayesian Model Averaging under Covariate Shift Pavel Izmailov Patrick K. Nicholson Sanae Lotfi A. Wilson OOD UQCV BDL 151 46 0 22 Jun 2021
Contrastive Learning of Generalized Game Representations C. Trivedi Antonios Liapis Georgios N. Yannakakis OCL SSL 71 13 0 18 Jun 2021
On Invariance Penalties for Risk Minimization Kia Khezeli Arno Blaas Frank Soboczenski N. Chia John Kalantari 58 16 0 17 Jun 2021
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization Nanyang Ye Kaican Li Haoyue Bai Runpeng Yu Lanqing Hong Fengwei Zhou Zhenguo Li Jun Zhu CML OOD 109 113 0 07 Jun 2021
Out-of-Distribution Generalization in Kernel Regression Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan OODD OOD 57 13 0 04 Jun 2021
Goal Misgeneralization in Deep Reinforcement Learning L. Langosco Jack Koch Lee D. Sharkey J. Pfau Laurent Orseau David M. Krueger 88 84 0 28 May 2021
Attention, please! A survey of Neural Attention Models in Deep Learning Alana de Santana Correia Esther Luna Colombini HAI 128 198 0 31 Mar 2021
Nonlinear Invariant Risk Minimization: A Causal Approach Chaochao Lu Yuhuai Wu Jośe Miguel Hernández-Lobato Bernhard Schölkopf CML OOD 132 52 0 24 Feb 2021