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cs.LG2023
Metalearning with Very Few Samples Per Task
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown +3
Metalearning and multitask learning are two frameworks for solving a group of related learning tasks more efficiently than we could hope to solve each of the individual tasks on th…
cs.LG2023★ 2 cited
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
Harsh Chaudhari, Giorgio Severi, Alina Oprea +1
The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training. One…