A new research paper explores the trade-offs between memory and batch processing in adaptive learning systems, specifically within the context of stochastic Lipschitz bandits. The study characterizes the minimax expected pseudo-regret for systems that retain a limited amount of state information and organize actions into committed batches. The findings reveal a novel penalty term that highlights the distinct roles of state width and update depth, demonstrating that these factors are not interchangeable. The research also shows how information routing constraints influence regret and the encoding of decisions, with matching policies managing verification statistics and active sets. AI
IMPACT This research contributes to the theoretical understanding of adaptive learning algorithms, potentially influencing the design of more efficient AI systems.
RANK_REASON The cluster contains a single academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- Influence Flower
- Lipschitz bandits
- ScienceCast
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