Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. - Kai Sheng Tai; Richard Socher; Christopher D. Manning (ACL (1), 2015)

There and Back Again: A General Approach to Learning Sparse Models. - Vatsal Sharan; Kai Sheng Tai; Peter Bailis; Gregory Valiant (CoRR, 2017)

Finding Heavily-Weighted Features in Data Streams. - Kai Sheng Tai; Vatsal Sharan; Peter Bailis; Gregory Valiant (CoRR, 2017)

Sketching Linear Classifiers over Data Streams. - Kai Sheng Tai; Vatsal Sharan; Peter Bailis; Gregory Valiant (SIGMOD Conference, 2018)

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. - Edward Gan; Jialin Ding 001; Kai Sheng Tai; Vatsal Sharan; Peter Bailis (Proc. VLDB Endow., 2018)

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. - Vatsal Sharan; Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2019)

Equivariant Transformer Networks. - Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2019)

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. - Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2021)

Statistical learning under resource constraints. - Kai Sheng Tai (2021)

Spartan: Differentiable Sparsity via Regularized Transportation. - Kai Sheng Tai; Tai-Peng Tian; Ser Nam Lim (NeurIPS, 2022)

Analyzing Populations of Neural Networks via Dynamical Model Embedding. - Jordan Cotler; Kai Sheng Tai; Felipe Hernández 004; Blake Elias; David Sussillo (CoRR, 2023)

uCAP: An Unsupervised Prompting Method for Vision-Language Models. - A. Tuan Nguyen; Kai Sheng Tai; Bor-Chun Chen; Satya Narayan Shukla; Hanchao Yu; Philip Torr 001; Tai-Peng Tian; Ser-Nam Lim (ECCV (74), 2024)

Multi-entity Video Transformers for Fine-Grained Video Representation Learning. - Matthew Walmer; Rose Catherine Kanjirathinkal; Kai Sheng Tai; Keyur Muzumdar; Tai-Peng Tian; Abhinav Shrivastava (CVPR Workshops, 2025)