Accelerating Transformer Point Process Sampling with Speculative Decoding
报告人： Feng Zhou（Renmin University of China）
时间：2025-10-16 15:30-17:00
地点：Room 217, Guanghua Building 2
Abstract:
We propose TPP-SD, a novel approach that accelerates Transformer temporal point process (TPP) sampling by adapting speculative decoding (SD) techniques from language models. By identifying the structural similarities between thinning algorithms for TPPs and speculative decoding for language models, we develop an efficient sampling framework that leverages a smaller draft model to generate multiple candidate events, which are then verified by the larger target model in parallel. TPP-SD maintains the same output distribution as autoregressive sampling while achieving significant acceleration. Experiments on both synthetic and real datasets demonstrate that our approach produces samples from identical distributions as standard methods, but with 2-6 $\times$ speedup. Our ablation studies analyze the impact of hyperparameters such as draft length and draft model size on sampling efficiency. TPP-SD bridges the gap between powerful Transformer TPP models and the practical need for rapid sequence sampling.
About the Speaker:
周峰，中国人民大学统计学院副教授，中国人民大学“杰出青年学者”，主要研究领域包括统计机器学习、贝叶斯方法、随机过程、大模型推理加速等，主持国家自然科学基金青年项目、面上项目，在JMLR、STCO、ICML、NeurIPS、ICLR、AAAI、KDD等国际期刊和会议上已发表论文30余篇，担任NeurIPS、ICLR、IJCAI、AISTATS等国际会议领域主席，国际期刊《Statistics and Computing》副主编，中国商业统计学会人工智能分会副秘书长、全国工业统计学教学研究会青年统计学家协会第二届理事会理事。
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