567 citations · 646 across the 17 of their papers we have counts for
4 papers · 1 filter
Farzi Data: Autoregressive Data Distillation
Noveen Sachdeva, Zexue He, Wang-Cheng Kang +3
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi,…
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta +4
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can compre…
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach +4
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems. A typical model ingests hundreds of feat…