arXiv Machine Learning
A Deterministic Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving
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arXiv Machine Learning
Aug 19
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
arXiv:2608.12134v2 Announce Type: replace-cross Abstract: We study nonnegative submodular maximization on $n$ elements subject to a general matroid of rank $k$, when the offline algorithm is given an...
arXiv AI
Jul 10
Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization
arXiv:2603. 00910v2 Announce Type: replace-cross Abstract: Layer-wise capacity in large language models is highly non-uniform: some layers contribute disproportionately to loss reduction, whereas others are nearly redundant.
arXiv Machine Learning
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Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
The paper proposes a method for routing requests to a fixed pool of quantized Mixture-of-Experts (MoE) instances, aiming to maximize throughput while respecting a quality‑degradation budget. It introduces Fragility‑Weighted Perplexity (FWP) as a request‑specific risk metric derived from prompt tokens, and uses a window‑level linear program to compute a reduced‑reward score that aligns with the LP optimum. Experiments on Qwen prompts show that FWP‑based allocation improves throughput by 2.5% over request‑agnostic mixing and static configurations.
arXiv AI
Aug 11
Adaptive Two-Level Allocation of a Conserved Capacity Budget Across Locations and Service Classes
arXiv:2608. 07747v1 Announce Type: new Abstract: We study how to share a single conserved capacity budget across many locations and two service classes when demand is uneven, time-varying, and can exceed supply.
arXiv Machine Learning
Sep 1
Optimally Selecting Representative Agents from a Metric Space
arXiv:2608.29097v1 Announce Type: cross Abstract: This paper studies the problem of proportionally fair clustering, where the goal is to select $k$ ``centers'' from a metric space that fairly represe...