arXiv:2602.11995v2 Announce Type: replace Abstract: In large-scale data processing scenarios, data often arrive in sequential streams generated by complex systems that exhibit drifting distributions and time-varying system parameters. This nonstationarity challenges theoretical analysis, as it violates classical assumptions of i.i.d. (independent and identically distributed) samples, necessitating algorithms capable of real-time updates without expensive retraining. An effective approach should process each sample in a single pass, while maintaining computational and memory complexities indepe
The increasing prevalence of large-scale data processing in dynamic environments, particularly in AI systems, necessitates robust theoretical frameworks for continuous learning algorithms.
Improved stability and tracking in non-stationary environments are crucial for developing more reliable and adaptive AI systems, especially those operating in real-world, dynamic settings.
This research provides a theoretical advancement in understanding how momentum-based learning algorithms perform beyond static data assumptions, enabling more effective real-time AI updates.
- · AI/ML Research Community
- · Developers of real-time AI systems
- · Industries with dynamic data streams (e.g., finance, autonomous systems)
- · Systems reliant on retraining for adaptation
- · Algorithms lacking theoretical guarantees in non-stationary settings
More stable and performant AI models in practical, non-stationary applications.
Accelerated development of AI agents that can continuously learn and adapt without extensive human oversight.
Reduced computational overhead and energy consumption for maintaining high-performing AI systems, potentially impacting compute infrastructure requirements.
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