Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion. - Zheng Zhang 005; Cole Hawkins (ICDM, 2018)

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models. - Chunfeng Cui; Cole Hawkins; Zheng Zhang 005 (ICCAD, 2019)

End-to-End Variational Bayesian Training of Tensorized Neural Networks with Automatic Rank Determination. - Cole Hawkins; Zheng Zhang 005 (CoRR, 2020)

3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low Bitwidth Quantization, and Ultra-Low Latency Acceleration. - Yao Chen 008; Cole Hawkins; Kaiqi Zhang 002; Zheng Zhang 005; Cong Hao (ACM Great Lakes Symposium on VLSI, 2021)

On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method. - Kaiqi Zhang 002; Cole Hawkins; Xiyuan Zhang 003; Cong Hao; Zheng Zhang 005 (CoRR, 2021)

3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration. - Yao Chen 008; Cole Hawkins; Kaiqi Zhang 002; Zheng Zhang 005; Cong Hao (CoRR, 2021)

Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation. - Cole Hawkins; Vassilis N. Ioannidis; Soji Adeshina; George Karypis (CoRR, 2021)

Low-Rank+Sparse Tensor Compression for Neural Networks. - Cole Hawkins; Haichuan Yang; Meng Li 004; Liangzhen Lai; Vikas Chandra (CoRR, 2021)

General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification. - Kaiqi Zhang 002; Cole Hawkins; Zheng Zhang 005 (Frontiers Artif. Intell., 2021)

Bayesian tensorized neural networks with automatic rank selection. - Cole Hawkins; Zheng Zhang 005 (Neurocomputing, 2021)

Online, Informative MCMC Thinning with Kernelized Stein Discrepancy. - Cole Hawkins; Alec Koppel; Zheng Zhang 005 (CoRR, 2022)

Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination. - Cole Hawkins; Xing Liu; Zheng Zhang 005 (SIAM J. Math. Data Sci., 2022)

Compressed Training for Uncertainty-Aware Compact Neural Networks. - Cole Hawkins (2022)

Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. - Qingru Zhang; Dhananjay Ram; Cole Hawkins; Sheng Zha; Tuo Zhao (EMNLP (Findings), 2023)

VCC: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens. - Zhanpeng Zeng; Cole Hawkins; Mingyi Hong 001; Aston Zhang; Nikolaos Pappas 004; Vikas Singh; Shuai Zheng (NeurIPS, 2023)

Online MCMC Thinning with Kernelized Stein Discrepancy. - Alec Koppel; Joe Eappen; Sujay Bhatt; Cole Hawkins; Sumitra Ganesh (SIAM J. Math. Data Sci., 2024)

Sequence-level Large Language Model Training with Contrastive Preference Optimization. - Zhili Feng; Dhananjay Ram; Cole Hawkins; Aditya Rawal; Jinman Zhao; Sheng Zha (NAACL (Findings), 2025)