Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. - Zhenyu Pan; Anshujit Sharma; Jerry Yao-Chieh Hu; Zhuo Liu; Ang Li 006; Han Liu 001; Michael C. Huang 001; Tong Geng (AAAI, 2023)

Feature Programming for Multivariate Time Series Prediction. - Alex Daniel Reneau; Jerry Yao-Chieh Hu; Ammar Gilani; Han Liu 001 (ICML, 2023)

On Sparse Modern Hopfield Model. - Jerry Yao-Chieh Hu; Donglin Yang; Dennis Wu; Chenwei Xu; Bo-Yu Chen; Han Liu 001 (NeurIPS, 2023)

Feature Programming for Multivariate Time Series Prediction. - Alex Daniel Reneau; Jerry Yao-Chieh Hu; Chenwei Xu; Weijian Li 002; Ammar Gilani; Han Liu 001 (CoRR, 2023)

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e. - Chenwei Xu; Jerry Yao-Chieh Hu; Aakaash Narayanan; Mattson Thieme; Vladimir Nagaslaev; Mark Austin; Jeremy Arnold; Jose Berlioz; Pierrick Hanlet; Aisha Ibrahim; Dennis Nicklaus; Jovan Mitrevski; et al. (CoRR, 2023)

STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction. - Dennis Wu; Jerry Yao-Chieh Hu; Weijian Li 002; Bo-Yu Chen; Han Liu 001 (ICLR, 2024)

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. - Jerry Yao-Chieh Hu; Pei-Hsuan Chang; Haozheng Luo; Hong-Yu Chen; Weijian Li 002; Wei-Po Wang; Han Liu 001 (ICML, 2024)

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis. - Jerry Yao-Chieh Hu; Thomas Lin; Zhao Song 002; Han Liu 001 (ICML, 2024)

Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models. - Dennis Wu; Jerry Yao-Chieh Hu; Teng-Yun Hsiao; Han Liu 001 (ICML, 2024)

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model. - Chenwei Xu; Yu-Chao Huang; Jerry Yao-Chieh Hu; Weijian Li 002; Ammar Gilani; Hsi-Sheng Goan; Han Liu 001 (ICML, 2024)

Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes. - Jerry Yao-Chieh Hu; Dennis Wu; Han Liu 001 (NeurIPS, 2024)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). - Jerry Yao-Chieh Hu; Weimin Wu; Zhuoru Li; Sophia Pi; Zhao Song 002; Han Liu 001 (NeurIPS, 2024)

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. - Jerry Yao-Chieh Hu; Pei-Hsuan Chang; Robin Luo; Hong-Yu Chen; Weijian Li 002; Wei-Po Wang; Han Liu 001 (CoRR, 2024)

Nonparametric Modern Hopfield Models. - Jerry Yao-Chieh Hu; Bo-Yu Chen; Dennis Wu; Feng Ruan; Han Liu 001 (CoRR, 2024)

Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens. - Jiahao Yu 001; Haozheng Luo; Jerry Yao-Chieh Hu; Wenbo Guo 002; Han Liu 001; Xinyu Xing 001 (CoRR, 2024)

Decoupled Alignment for Robust Plug-and-Play Adaptation. - Haozheng Luo; Jiahao Yu 001; Wenxin Zhang; Jialong Li; Jerry Yao-Chieh Hu; Xinyu Xing 001; Han Liu 001 (CoRR, 2024)

Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. - Jerry Yao-Chieh Hu; Maojiang Su; En-Jui Kuo; Zhao Song 002; Han Liu 001 (CoRR, 2024)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). - Jerry Yao-Chieh Hu; Weimin Wu; Zhuoru Li; Zhao Song 002; Han Liu 001 (CoRR, 2024)

Differentially Private Kernel Density Estimation. - Erzhi Liu; Jerry Yao-Chieh Hu; Alex Daniel Reneau; Zhao Song 002; Han Liu 001 (CoRR, 2024)

On Differentially Private String Distances. - Jerry Yao-Chieh Hu; Erzhi Liu; Han Liu 001; Zhao Song 002; Lichen Zhang 003 (CoRR, 2024)

Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training. - Weimin Wu; Maojiang Su; Jerry Yao-Chieh Hu; Zhao Song 002; Han Liu 001 (CoRR, 2024)

AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies. - Yibo Wen; Chenwei Xu; Jerry Yao-Chieh Hu; Han Liu 001 (CoRR, 2024)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models. - Jerry Yao-Chieh Hu; Maojiang Su; En-Jui Kuo; Zhao Song 002; Han Liu 001 (ICLR, 2025)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency. - Jerry Yao-Chieh Hu; Wei-Po Wang; Ammar Gilani; Chenyang Li; Zhao Song 002; Han Liu 001 (ICLR, 2025)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality. - Jerry Yao-Chieh Hu; Weimin Wu; Yi-Chen Lee; Yu-Chao Huang; Minshuo Chen; Han Liu 001 (ICLR, 2025)

NdLinear Is All You Need for Representation Learning. - Alex Daniel Reneau; Jerry Yao-Chieh Hu; Zhongfang Zhuang; Ting-Chun Liu (CoRR, 2025)

Universal Approximation with Softmax Attention. - Jerry Yao-Chieh Hu; Hude Liu; Hong-Yu Chen; Weimin Wu; Han Liu (CoRR, 2025)

Attention Mechanism, Max-Affine Partition, and Universal Approximation. - Hude Liu; Jerry Yao-Chieh Hu; Zhao Song 002; Han Liu 001 (CoRR, 2025)

Fast and Low-Cost Genomic Foundation Models via Outlier Removal. - Haozheng Luo; Chenghao Qiu; Maojiang Su; Zhihan Zhou 001; Zoe Mehta; Guo Ye; Jerry Yao-Chieh Hu; Han Liu 001 (CoRR, 2025)

Latent Variable Estimation in Bayesian Black-Litterman Models. - Thomas Y. L. Lin; Jerry Yao-Chieh Hu; Paul W. Chiou; Peter Lin (CoRR, 2025)

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions. - Jerry Yao-Chieh Hu; Xiwen Zhang; Maojiang Su; Zhao Song 002; Han Liu 001 (CoRR, 2025)