Ye Yuan
My name is Ye Yuan. I am a second-year Ph.D. student at McGill University, and I’m fortunate to be supervised by Prof. Xue (Steve) Liu. I obtained my Bachelor of Science Degree in Honours Computer Science from McGill University as well, with distinction and first-class honours.
What truly captivates me is the potential of intelligent systems to assist humans. How can artificial intelligence accurately and flawlessly complete tasks assigned by humans? More specifically, my research is concentrated on (i) addressing the issue of out-of-distribution challenges in Offline Black Box Optimization through the use of proxy functions or conditional generative models, (ii) developing a foundational knowledge model, which involves using a neural network to represent knowledge, extracting information from unstructured text, and filling in missing information in a knowledge base, as well as (iii) improve the efficiency of language model’s inference with information compression or knowledge distillation.
My greatest assets are my eagerness to learn new things, my curiosity to delve into uncharted territories, and my drive to stay abreast of the latest developments.
Beyond academia, I collaborate closely with Microsoft Research Cambridge and Noah’s Ark Lab Montreal.
If you have any questions about my projects or have cooperation intentions with me, feel free to contact me via email: ye.yuan3 AT mail DOT mcgill DOT ca
Selected Publication:
* denotes co-first author and † denotes (co-)corresponding author.
Structured Entity Extraction Using Large Language Models. Ye Yuan*†, Haolun Wu*†, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhakar Mitra†. Arxiv Preprint Release.
Importance-Aware Co-Teaching for Offline Model-Based Optimization. Ye Yuan*, Can Chen*†, Zixuan Liu, Willie Neiswanger, Xue Liu. In NeurIPS 2023.