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arXiv Machine Learning By Heng Yao, Siyun Hou, Tianying Liu, Yulou Shu, Yong He, Chuan Yuan, Kaibin Qiu, Guowei Chen, Jiayu Zhao, Chao Yu, Ke Ding

PRIME: Mitigating Subgroup Optimization Competition in Shared CTR Top Networks with Plug-in Residual Input-Conditioned Mixture of Expert

Read the original on arXiv Machine Learning →

PRIME is a plug‑in residual input‑conditioned mixture of experts that preserves the original dense prediction path while adding low‑rank, input‑dependent logit corrections. By initializing residuals to zero, PRIME matches the baseline dense model at training start and stabilizes conditional estimation with multi‑bag aggregation and EMA load biases. Experiments on Avazu and Criteo across 13 CTR architectures show modest AUC and LogLoss gains, with PRIME outperforming APG on FiBiNET and DCNv2 while using fewer parameters and lower latency.

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.

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