arXiv Machine Learning
A Deterministic Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving
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arXiv Machine Learning
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A Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving
arXiv:2608. 16947v1 Announce Type: cross Abstract: Huang, Lou, and Xiao introduced Dynamic Mixture-of-Experts Serving and gave an O(sqrt(log k))-competitive randomized algorithm for its integral primal problem, where k is the number of replica GPUs beyond the mandatory copy of each expert.
arXiv AI
Sep 10
Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids, and Full-Bandit Learning
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Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization
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arXiv Machine Learning
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Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories
The paper studies how to allocate a fixed computational budget across the denoising steps of diffusion models to improve sample quality at deployment. It shows that the expected benefit of evaluating multiple candidates at a step can be decomposed into a step‑specific sensitivity and a universal sample‑size factor, and that the optimal allocation follows a water‑filling structure. Experiments demonstrate that this allocation achieves the same quality as a uniform strategy while reducing function evaluations by 20–50%.
arXiv Machine Learning
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Learning to Allocate Resources with Censored Feedback
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arXiv Machine Learning
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Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
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