no code implementations • 15 Feb 2024 • Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H. Chi, James Caverlee, Julian McAuley, Derek Zhiyuan Cheng
The training of large language models (LLMs) is expensive.
no code implementations • 15 Oct 2023 • Noveen Sachdeva, Zexue He, Wang-Cheng Kang, Jianmo Ni, Derek Zhiyuan Cheng, Julian McAuley
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure.
no code implementations • 27 May 2023 • Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
no code implementations • NeurIPS 2023 • Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
no code implementations • 10 May 2023 • Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, Derek Zhiyuan Cheng
In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.
no code implementations • 25 Oct 2022 • Yin Zhang, Ruoxi Wang, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi, Derek Zhiyuan Cheng
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
no code implementations • 29 Oct 2020 • Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi
It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
no code implementations • 21 Oct 2020 • Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi
Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
no code implementations • 17 Aug 2020 • Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
1 code implementation • 25 Jul 2020 • Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
no code implementations • 20 Feb 2020 • Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, Ed H. Chi
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
2 code implementations • ACM Conference on Recommender Systems 2019 • Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Ajit Kumthekar, Zhe Zhao, Li Wei, Ed Chi
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.