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cs.LG2026
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
Chenxuanyin Zou, Jiayang Ren, Qiangqiang Mao +3
Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and sh…
cs.LG2026
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jing Liu, Chenxuanyin Zou, Jiayang Ren +5
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obsta…