Joint KL yields horizon-free approximation but an information-theoretic lower bound of order Omega(H) for estimation error in autoregressive learning, with matching computationally efficient upper bounds.
Proceedings of the 20th international conference on machine learning (icml-03) , pages=
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Presents the first online Learning-to-Defer algorithm achieving regret O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
A projection-based algorithm for COCO achieves O(log T) regret and O(log T) CCV for strongly convex losses and O(sqrt(T)) for convex losses by leveraging self-contracted curves.
BalanceRoute uses a piecewise-linear F-score (with optional short lookahead) for sticky request routing in LLM serving, reducing DP imbalance and raising end-to-end throughput versus vLLM baselines on production and Azure traces.
citing papers explorer
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Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
Joint KL yields horizon-free approximation but an information-theoretic lower bound of order Omega(H) for estimation error in autoregressive learning, with matching computationally efficient upper bounds.
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Online Learning-to-Defer with Varying Experts
Presents the first online Learning-to-Defer algorithm achieving regret O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
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Improved Guarantees for Constrained Online Convex Optimization via Self-Contraction
A projection-based algorithm for COCO achieves O(log T) regret and O(log T) CCV for strongly convex losses and O(sqrt(T)) for convex losses by leveraging self-contracted curves.
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Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale
BalanceRoute uses a piecewise-linear F-score (with optional short lookahead) for sticky request routing in LLM serving, reducing DP imbalance and raising end-to-end throughput versus vLLM baselines on production and Azure traces.