May 13, 2021 · We demonstrate that our algorithm is superior to state of the art agnostic active learning algorithms on image classification datasets.

Empirically, we demonstrate that our algorithm is superior to state of the art agnostic active learning algorithms on image classification datasets. 1.

May 13, 2021 · Besides being computationally efficient, our approach avoids the need to tune hyper- parameters and the use of a constrained empirical risk.

May 13, 2021 · Empirically, we demonstrate that our algorithm is superior to state of the art agnostic active learning algorithms on image classification ...

Jan 1, 2021 · Katz-Samuels, Julian, Zhang, Jifan, Jain, Lalit, and Jamieson, Kevin. "Improved Algorithms for Agnostic Pool-based Active Classification".

Apr 18, 2023 · Abstract: We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active ...

Improved Algorithms for Agnostic Pool-based Active Classification. File Structure. iwal/ Our implementation of IWAL based methods, see iwal/README.md for ...

Co-authors ; Improved Algorithms for Agnostic Pool-based Active Classification. J Katz-Samuels, J Zhang, L Jain, K Jamieson. ICML 2021, 2021. 26, 2021 ; Galaxy: ...

Improved Algorithms for Agnostic Pool-based Active Classification, Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson, ICML 2021. PDF. High ...

Proceedings of the 39th International Conference on Machine Learning (ICML 2022). Improved Algorithms for Agnostic Pool-based Active Classification PDF