arXiv:2606.07589v1 Announce Type: new Abstract: Sequential filtering pipelines are a common design pattern in large-scale systems, where a large population of items is progressively reduced by a sequence of stages that each incur cost. Despite their prevalence in ranking systems, cascaded machine learning inference, and fraud detection, filter ordering is often determined by heuristics without formal guarantees. We formalize sequential filtering under an expected-cost objective and prove that, under an independence model, ordering filters by increasing ratio of cost to rejection probability mi
The increasing complexity and scale of AI systems and data processing demand more efficient filtering mechanisms to manage computational resources.
This research provides a formal guarantee for optimizing sequential filtering pipelines, which can lead to significant cost reductions and performance improvements in large-scale AI applications.
The formalization offers a principled approach to filter ordering, moving beyond heuristic methods to theoretically optimal strategies for cost and rejection efficiency.
- · Large-scale AI systems
- · Cloud providers
- · Fraud detection services
- · Inefficient heuristic-based systems
- · High-cost data processing infrastructures
Improved efficiency and reduced operational costs for systems heavily reliant on sequential data filtering.
Accelerated development of more complex and resource-intensive AI applications due to lower inference and processing overheads.
Potential for new AI services that were previously cost-prohibitive due to the inefficiency of existing filtering methods.
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Read at arXiv cs.LG