4 papers
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
VirtualMLE: A Virtual ML Engineer that Optimizes Sequential Recommenders
Shiteng Cao, Jingwen Liu, Junda She +1
Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, reflection, and tool utilization, unlocking new paradigms for automating…
Strategic Stalemates: The Paradox of Export Controls in the U.S.-China AI Race
Jingwen Liu, Jyh-An Lee
Export control is a policy and legal tool to protect national interests by regulating exports of sensitive goods and technology to foreign nations. It has become central to U.S.-Ch…
Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases
Jingwen Liu, Ezra Edelman, Surbhi Goel +1
This work investigates the ``small-vs-large gap'', where repeating on fewer samples can lead to compute saving during training compared to using a larger dataset. This is observed…