arXiv:2606.11130v1 Announce Type: new Abstract: We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. In the passive learning setting, recent work gave a computationally efficient algorithm that uses $poly(d,1/\epsilon)$ labeled examples and outputs a hypothesis with error $O(opt)+\epsilon$, where $opt$ is the squared loss of the best fit ReLU. Here we focus on the interactive setting, where the learner has some form of query access to the labels of unlabeled examples. Our main result is the first
This research is part of ongoing efforts within the AI community to improve the robustness and efficiency of machine learning algorithms, particularly in foundational areas like regression with ReLUs.
Improving the robustness of foundational AI models like ReLUs can lead to more reliable and efficient AI systems, especially in scenarios with noisy or limited data. This underpins the broader advancement of AI capabilities.
The development of more efficient algorithms for learning general ReLUs will enable better performance in complex statistical learning tasks, potentially reducing the computational resources and data required.
- · AI researchers
- · Machine learning developers
More robust and efficient AI models for various applications including computer vision and natural language processing.
Reduced computational costs and data requirements for training certain types of neural networks.
Accelerated development of AI systems based on improved foundational algorithms, potentially impacting AI scalability and accessibility.
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