← Back to all news
arXiv Machine Learning By Yizhou Ma, Wenbo Wu, Xikun Jiang, Zhuoqin Yang, Luis-Daniel Ib\'a\~nez

Cost-Aware Mixture-of-Experts Coordination for Model Markets

Read the original on arXiv Machine Learning →

The paper introduces a Mixture-of-Experts (MoE) framework for model marketplaces, treating MoE as a market-level coordination mechanism rather than a learning architecture. Brokers use gating networks to combine heterogeneous experts into a composite service, while a cost-aware gating mechanism and a revenue allocation rule distribute residual revenue based on expert participation and execution cost. Experiments on fifteen tabular and image benchmarks show that MoE Market achieves higher welfare and lower expected cost than standard MoE, with competitive predictive performance and reduced computational overhead compared to exact Shapley allocation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
6d ago

Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models

The paper introduces the Constrained Triple-Head Price Optimization (C3PO) network, a transformer-based model designed for real‑time bi‑level decision making in discrete‑choice settings. C3PO integrates price imitation learning, multi‑task revenue prediction, and in‑context elasticity learning, while a lightweight differentiable constraint layer enforces business pricing rules. Experiments on simulated, synthetic, and real‑world datasets show that C3PO outperforms calibrated MNL baselines by up to 7.9 percentage points in expected revenue and reduces constraint violations by over 50%, with significant improvements in healthcare tender and airline ancillary pricing applications.

By Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam
llmsfine-tuningbenchmarks
More like this →
arXiv Machine Learning
Jul 14

Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources

arXiv:2607. 09802v1 Announce Type: new Abstract: The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply.

By Balasubramanian Sivan, Renato Paes Leme, Mihai Tiuca, Ian McFarlane, Vasilis Gkatzelis, Nehal Mehta, Soheil Hassas Yeganeh, Vahab Mirrokni, Amin Vahdat
safety
More like this →
arXiv AI
Sep 21

OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios

OneBid is a unified auto‑bidding foundation model that consolidates diverse cost‑per‑X (oCPX) advertising scenarios into a single framework. It builds on Decision Transformer by conditioning on two atomic signals—Return‑to‑Go for conversion value and Cost‑to‑Go for cost ratio—and incorporates value‑aware regularization. A sequence‑level Mixture‑of‑Experts architecture captures cross‑scenario knowledge while preserving low latency, and a Critic‑guided Relative Offline Policy optimization (CROP) aligns the backbone with scenario‑specific preferences without unsafe online exploration. In production at Kuaishou, OneBid achieved a 2.2% overall ADVV increase and up to 13.1% in the ROAS scenario.

By Yewen Li, Peng Jiang, Yitian Li, Pengfei Lv, Xialong Liu, Peng Jiang, Qingpeng Cai
llmsreinforcement-learningfine-tuning
More like this →
Hugging Face Trending Papers
Jun 8

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability.

efficiencybenchmarks
More like this →