{"total":14,"rows":[{"id":"2306.15063","title":"Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression","created":"2023-06-26","cited_by_count":146,"area":null},{"id":"2501.11039","title":"Model Predictive Task Sampling for Efficient and Robust Adaptation","created":"2025-01-19","cited_by_count":21,"area":null},{"id":"2307.08924","title":"Towards Task Sampler Learning for Meta-Learning","created":"2023-07-18","cited_by_count":11,"area":null},{"id":"2410.21698","title":"On the Role of Depth and Looping for In-Context Learning with Task Diversity","created":"2024-10-29","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":"2306.09222","title":"Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization","created":"2023-06-15","cited_by_count":6,"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":"2503.09679","title":"DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2311.02879","title":"Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2404.16814","title":"Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution","created":"2024-04-25","cited_by_count":1,"area":null},{"id":"2509.18349","title":"Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2601.09018","title":"Meta-learning to Address Data Shift in Time Series Classification","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2608.15504","title":"PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2610.14913","title":"Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}