{"total":39,"rows":[{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2302.11861","title":"Out-of-Domain Robustness via Targeted Augmentations","created":"2023-02-23","cited_by_count":24,"area":null},{"id":"2210.00706","title":"Information-Theoretic Analysis of Unsupervised Domain Adaptation","created":"2022-10-03","cited_by_count":15,"area":null},{"id":"2203.03059","title":"Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?","created":"2022-03-06","cited_by_count":13,"area":null},{"id":"2206.13482","title":"Understanding Benign Overfitting in Gradient-Based Meta Learning","created":"2022-06-27","cited_by_count":11,"area":null},{"id":"2210.06511","title":"Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness","created":"2022-10-12","cited_by_count":11,"area":null},{"id":"2211.07206","title":"Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice","created":"2022-11-14","cited_by_count":11,"area":null},{"id":"2312.13978","title":"Metalearning with Very Few Samples Per Task","created":"2023-12-21","cited_by_count":11,"area":null},{"id":"2402.04054","title":"More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms","created":"2024-02-06","cited_by_count":11,"area":null},{"id":"2208.01545","title":"The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence","created":"2022-08-02","cited_by_count":10,"area":null},{"id":"2303.11183","title":"Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning","created":"2023-03-20","cited_by_count":10,"area":null},{"id":"2310.00708","title":"A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm","created":"2023-10-01","cited_by_count":10,"area":null},{"id":"2206.09136","title":"Provable Generalization of Overparameterized Meta-learning Trained with SGD","created":"2022-06-18","cited_by_count":9,"area":null},{"id":"2208.03848","title":"Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality","created":"2022-08-08","cited_by_count":9,"area":null},{"id":"2302.11709","title":"Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes","created":"2023-02-23","cited_by_count":8,"area":null},{"id":"2303.04756","title":"Meta-learning Control Variates: Variance Reduction with Limited Data","created":"2023-03-08","cited_by_count":7,"area":null},{"id":"2304.02064","title":"Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation","created":"2023-04-04","cited_by_count":7,"area":null},{"id":"2306.13841","title":"Is Pre-training Truly Better Than Meta-Learning?","created":"2023-06-24","cited_by_count":6,"area":null},{"id":"2501.06167","title":"Meta-Learning for Physically-Constrained Neural System Identification","created":"2025-01-10","cited_by_count":6,"area":null},{"id":"2410.22788","title":"Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning","created":"2024-10-30","cited_by_count":4,"area":null},{"id":"2502.01117","title":"Learning to Learn Weight Generation via Local Consistency Diffusion","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2509.13185","title":"Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy","created":"2025-09-16","cited_by_count":4,"area":null},{"id":"2406.09745","title":"How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis","created":"2024-06-14","cited_by_count":3,"area":null},{"id":"2406.11569","title":"Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs","created":"2024-06-17","cited_by_count":3,"area":null},{"id":"2506.07861","title":"Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective","created":"2025-06-09","cited_by_count":3,"area":null},{"id":"2212.06461","title":"A Statistical Model for Predicting Generalization in Few-Shot Classification","created":"2022-12-13","cited_by_count":2,"area":null},{"id":"2304.14332","title":"On the Generalization Error of Meta Learning for the Gibbs Algorithm","created":"2023-04-27","cited_by_count":2,"area":null},{"id":"2311.04561","title":"Information-Theoretic Generalization Bounds for Transductive Learning and its Applications","created":"2023-11-08","cited_by_count":2,"area":null},{"id":"2502.01458","title":"The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibration","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2411.17898","title":"On the ERM Principle in Meta-Learning","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2510.19797","title":"Transformers are almost optimal metalearners for linear classification","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2306.08460","title":"Improving Generalization in Meta-Learning via Meta-Gradient Augmentation","created":"2023-06-14","cited_by_count":0,"area":null},{"id":"2408.05992","title":"Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2411.06746","title":"Neuromodulated Meta-Learning","created":"2024-11-11","cited_by_count":0,"area":null},{"id":"2510.07509","title":"Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shift","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2510.15422","title":"Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2512.13935","title":"Informing Acquisition Functions via Foundation Models for Molecular Discovery","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2602.19788","title":"Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2606.02008","title":"Provable Data Scaling Law for Meta Learning via Complexity Minimization","created":"2026-06-01","cited_by_count":0,"area":null}],"next_cursor":null}