by instadatahelp | Aug 31, 2023 | AI Blogs
On Batch Teaching Without Collusion Authors: Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles; Volume 24, Issue 40, Pages 1-33, 2023. Abstract In order to prevent collusion in formal models of learning from teachers, certain criteria...
by instadatahelp | Aug 31, 2023 | AI Blogs
arXivLabs is a platform that allows collaborators to create and share new features on our website. Both individuals and organizations who work with arXivLabs have fully embraced and supported our principles of openness, community, excellence, and user data privacy. We...
by instadatahelp | Aug 31, 2023 | AI Blogs
Beyond Stationary Points: A Non-parametric Perspective on FedAvg and FedProx Authors: Lili Su, Jiaming Xu, Pengkun Yang; Journal of Machine Learning Research (JMLR), 24(203):1−48, 2023. Abstract Federated Learning (FL) is a decentralized learning framework with...
by instadatahelp | Aug 31, 2023 | AI Blogs
In large-scale unsupervised datasets, Semi-supervised Learning (SSL) has a vulnerability to out-of-distribution (OOD) samples. This is because it mistakenly labels OOD samples as in-distribution (ID) due to over-confidence in pseudo-labeling. The problem stems from...
by instadatahelp | Aug 31, 2023 | AI Blogs
Robust Load Balancing with Machine Learned Advice Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng; 24(44):1−46, 2023. Abstract This study introduces and examines a theoretical model for load balancing of large databases, specifically commercial search...
