Author

M. Bateni

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#artificial intelligence Preprint Oct 2026

BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models

Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers can yield imbalanced expert utilization and treat experts as an unstructured collection, whose indices carry no topological meaning. We introduce {\bf BRANCH-MoE}, a rou...

Gang Fu, Adel Javanmard, M. Bateni et al. · 0 citations

#machine learning Preprint Sep 2026

Optimal Networks for Agentic Information Aggregation

We study information aggregation in the networked learning model introduced by Kearns, Roth, and Ryu (SODA 2026). There is a fixed distribution over $d$ features and a common label. Agents learn in topological order on a directed acyclic graph. Each observes a subset of the features and its parents'predictions, fits a...

M. Bateni, Z. Hadizadeh, Mohammadtaghi Hajiaghayi et al. · 0 citations