arXiv:2606.11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gradient-enhanced surrogate loss that approximates the cumulative loss using only historical summaries, which modifies and improves upon the existing renewable estimation approach for the same model in the high-dimensional setting, and removes the batch-number constraint in previous studies. We then extend the method to distributed streaming data under the master-client architecture, where batches a
The continuous growth of streaming data in high-dimensional environments necessitates more efficient and scalable online estimation techniques.
Improved online estimation in high-dimensional models can significantly enhance the performance and resource efficiency of AI systems dealing with real-time data.
The removal of batch-number constraints and the introduction of a gradient-enhanced approach offer more flexible and powerful tools for online learning in distributed settings.
- · Machine learning researchers
- · Cloud computing providers
- · AI-driven financial services
- · Real-time analytics platforms
- · Legacy batch processing systems
- · Inefficient online learning algorithms
More robust and scalable AI models can be deployed in streaming data applications without performance bottlenecks.
This could accelerate the development of real-time autonomous AI agents capable of continuous learning and adaptation.
The widespread adoption of such methods might lead to a re-evaluation of current computational paradigms for large-scale data processing, favoring continuous over batch approaches.
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Read at arXiv cs.LG