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Online Learning for Multi-Layer Hierarchical Inference under Partial and Policy-Dependent Feedback
arXiv – CS AI|Haoran Zhang, Seohyeon Cha, Hasan Burhan Beytur, Kevin S Chan, Gustavo de Veciana, Haris Vikalo|
🤖AI Summary
Researchers developed a new variance-reduced EXP4-based algorithm for optimizing routing policies in multi-layer hierarchical inference systems. The solution addresses the challenge of sparse, policy-dependent feedback in AI systems where prediction errors are only revealed at terminal layers, improving stability and performance over standard importance-weighted approaches.
Key Takeaways
- →Multi-layer hierarchical inference systems face challenges with partial feedback that only occurs at terminal oracle layers.
- →Standard importance-weighted contextual bandit methods become unstable as feedback probability decays along the hierarchy.
- →A new variance-reduced EXP4-based algorithm integrated with Lyapunov optimization provides unbiased loss estimation.
- →The algorithm demonstrates improved stability and performance on large-scale multi-task workloads compared to existing approaches.
- →The research provides regret guarantees and establishes near-optimality under stochastic arrivals and resource constraints.
#machine-learning#hierarchical-inference#online-learning#routing-optimization#contextual-bandits#variance-reduction#feedback-systems#computational-efficiency
Read Original →via arXiv – CS AI
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