Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach

Yinan Li, Chicheng Zhang
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4744-4752, 2024.

Abstract

We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise (Tsybakov, 2004) under structured unlabeled data distributions. Inspired by Diakonikolas et al., (2020c), we prove that any approximate first-order stationary point of a smooth nonconvex loss function yields a halfspace with a low excess error guarantee. In light of the above structural result, we design a nonconvex optimization-based algorithm with a label complexity of $\tilde{O}(d (\frac{1}{\epsilon})^{\frac{8-6\alpha}{3\alpha-1}})$, under the assumption that the Tsybakov noise parameter $\alpha \in (\frac13, 1]$, which narrows down the gap between the label complexities of the previously known efficient passive or active algorithms (Diakonikolas et al., 2020b; Zhang and Li, 2021) and the information-theoretic lower bound in this setting.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-li24p, title = { Efficient Active Learning Halfspaces with {T}sybakov Noise: A Non-convex Optimization Approach }, author = {Li, Yinan and Zhang, Chicheng}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {4744--4752}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/li24p/li24p.pdf}, url = {https://proceedings.mlr.press/v238/li24p.html}, abstract = { We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise (Tsybakov, 2004) under structured unlabeled data distributions. Inspired by Diakonikolas et al., (2020c), we prove that any approximate first-order stationary point of a smooth nonconvex loss function yields a halfspace with a low excess error guarantee. In light of the above structural result, we design a nonconvex optimization-based algorithm with a label complexity of $\tilde{O}(d (\frac{1}{\epsilon})^{\frac{8-6\alpha}{3\alpha-1}})$, under the assumption that the Tsybakov noise parameter $\alpha \in (\frac13, 1]$, which narrows down the gap between the label complexities of the previously known efficient passive or active algorithms (Diakonikolas et al., 2020b; Zhang and Li, 2021) and the information-theoretic lower bound in this setting. } }
Endnote
%0 Conference Paper %T Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach %A Yinan Li %A Chicheng Zhang %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-li24p %I PMLR %P 4744--4752 %U https://proceedings.mlr.press/v238/li24p.html %V 238 %X We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise (Tsybakov, 2004) under structured unlabeled data distributions. Inspired by Diakonikolas et al., (2020c), we prove that any approximate first-order stationary point of a smooth nonconvex loss function yields a halfspace with a low excess error guarantee. In light of the above structural result, we design a nonconvex optimization-based algorithm with a label complexity of $\tilde{O}(d (\frac{1}{\epsilon})^{\frac{8-6\alpha}{3\alpha-1}})$, under the assumption that the Tsybakov noise parameter $\alpha \in (\frac13, 1]$, which narrows down the gap between the label complexities of the previously known efficient passive or active algorithms (Diakonikolas et al., 2020b; Zhang and Li, 2021) and the information-theoretic lower bound in this setting.
APA
Li, Y. & Zhang, C.. (2024). Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:4744-4752 Available from https://proceedings.mlr.press/v238/li24p.html.

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