4 papers
An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks
Sheng Lun Christine Cao, Destenie Nock, Alex Davis
Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the…
Supracompetitive Pricing Under AI Monoculture
Shengyu Cao, Ming Hu
When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller…
STEM: Scaling Transformers with Embedding Modules
Ranajoy Sadhukhan, Sheng Cao, Harry Dong +5
Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication ove…
Param for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
Sheng Cao, Mingrui Wu, Karthik Prasad +2
The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it…