[2006.15785] A No|Free|Lunch Theorem for MultiTask Learning

[2006.15785] A No-Free-Lunch Theorem for MultiTask Learning - arXiv

A simple rank-based procedure can achieve near optimal aggregations of tasks' datasets, despite a search space exponential in N.

A No-Free-Lunch Theorem for MultiTask Learning - ResearchGate

... 2006.15785v4 [cs.LG] 5 Aug 2020. APREPRINT - AUGUST 7, 2020. it might seem at ﬁrst that there is a gap in either algorithmic approaches ...

A No-Free-Lunch Theorem for MultiTask Learning - Semantic Scholar

A No-Free-Lunch Theorem for MultiTask Learning · Steve Hanneke, Samory Kpotufe · Published in Annals of Statistics 29 June 2020 · Computer Science, Mathematics.

arXiv:2006.15785v4 [cs.LG] 5 Aug 2020

A NO-FREE-LUNCH THEOREM FOR MULTITASK LEARNING. A PREPRINT. Steve ... to no prior distributional information. Many interesting messages ...

A no-free-lunch theorem for multitask learning | Request PDF

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A No-Free-Lunch Theorem for MultiTask Learning. - dblp

Steve Hanneke, Samory Kpotufe: A No-Free-Lunch Theorem for MultiTask Learning. CoRR abs/2006.15785 (2020). manage site settings. To protect your privacy, ...

Mathematics Jun 2020 - arXiv

... Learning (stat.ML). [4380] arXiv:2006.15785 (cross-list from cs.LG) [pdf, other]. Title: A No-Free-Lunch Theorem for MultiTask Learning. Steve Hanneke, Samory ...

Multitask Learning and Bandits via Robust Statistics - Hamsa Bastani

A no-free-lunch theorem for multitask learning. arXiv preprint. arXiv:2006.15785 . Hardt, Moritz, Eric Price, Nati Srebro. 2016. Equality of opportunity in ...

arXiv:2303.04338v1 [cs.LG] 8 Mar 2023 - NSF PAR

ing multiple tasks over multiple paths (Multipath MTL). In conjunction ... A no-free-lunch theorem for multitask learning. arXiv preprint arXiv:2006.15785.

Provable Pathways: Learning Multiple Tasks over Multiple Paths

2020. A no-free-lunch theorem for multitask learning. arXiv preprint arXiv:2006.15785. He, C.; Zheng, S.

Transfer Learning in Large-scale Gaussian Graphical Models with ...

... multiple graphs when data from multiple studies are available. For ... A no-free-lunch theorem for multitask learning. arXiv:2006.15785.

No Free Lunch Theorem for Machine Learning

The theorem states that all optimization algorithms perform equally well when their performance is averaged across all possible problems.

Estimation and Inference for High-Dimensional Generalized Linear ...

A no-free-lunch theorem for multitask learning. arXiv:2006.15785. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning ...

theoretical bounds on estimation error - Mengye Ren

Steve Hanneke and Samory Kpotufe. A no-free-lunch theorem for multitask learning. arXiv preprint. arXiv:2006.15785, 2020. Rong Jin, Shijun Wang, and ...

Model Zoo: A Growing “Brain" That Learns Continually - OpenReview

learning algorithm. In NeurIPS. 168. Hanneke, S. and Kpotufe, S. (2020). A no-free-lunch theorem for multitask learning. arXiv preprint. 169. arXiv:2006.15785.

How Fine-Tuning Allows for Effective Meta-Learning

A no-free-lunch theorem for multitask learning. arXiv preprint. arXiv:2006.15785, 2020a. Hanneke, S. and Kpotufe, S. On the value of target data in transfer ...

Minimax Estimation for Personalized Federated Learning

they termed federated learning, for collaboratively training machine learning ... A no-free-lunch theorem for multitask learning. arXiv preprint arXiv:2006.15785, ...

A Picture of the Space of Typical Learnable Tasks

probabilistic models during training but learn simi- lar models ... A no-free-lunch theorem for multitask learning. arXiv preprint arXiv:2006.15785,. 2020.

A Distribution Dependent Analysis of Meta-Learning Mikhail ... - ERA

Kpotufe, A no-free-lunch theorem for multi-task learn- ing, arXiv:2006.15785, 2020. [21] M. Khodak, M.-F. Balcan, and A. Talwalkar, “Provable guarantees for.

Learning Across Bandits in High Dimension via Robust Statistics

A no-free-lunch theorem for multitask learning. arXiv preprint. arXiv:2006.15785 . Hardt, Moritz, Eric Price, Nati Srebro. 2016. Equality of ...