Question Type Guided Attention in Visual Question Answering. - Yang Shi 005; Tommaso Furlanello; Sheng Zha; Animashree Anandkumar (ECCV (4), 2018)

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. - He He 001; Sheng Zha; Haohan Wang (DeepLo@EMNLP-IJCNLP, 2019)

Dive into Deep Learning for Natural Language Processing. - Haibin Lin; Xingjian Shi; Leonard Lausen; Aston Zhang; He He 001; Sheng Zha; Alexander J. Smola (EMNLP/IJCNLP (2), 2019)

Just-in-Time Dynamic-Batching. - Sheng Zha; Ziheng Jiang; Haibin Lin; Zhi Zhang (CoRR, 2019)

Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources. - Haibin Lin; Hang Zhang 005; Yifei Ma; Tong He 002; Zhi Zhang 005; Sheng Zha; Mu Li 003 (CoRR, 2019)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (CoRR, 2019)

Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes. - Shuai Zheng 004; Haibin Lin; Sheng Zha; Mu Li 003 (CoRR, 2020)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (J. Mach. Learn. Res., 2020)

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. - Haoyu He; Xingjian Shi; Jonas Mueller 001; Sheng Zha; Mu Li 003; George Karypis (SustaiNLP@EMNLP, 2021)

Meta-learning via Language Model In-context Tuning. - Yanda Chen; Ruiqi Zhong; Sheng Zha; George Karypis; He He 001 (ACL (1), 2022)

Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. - Vishakh Padmakumar; Leonard Lausen; Miguel Ballesteros; Sheng Zha; He He 001; George Karypis (NAACL-HLT, 2022)

Differentially Private Bias-Term only Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2022)

Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic. - Soumajyoti Sarkar; Kaixiang Lin; Sailik Sengupta; Leonard Lausen; Sheng Zha; Saab Mansour (CoRR, 2022)

Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion. - Hengzhi Pei; Jinman Zhao; Leonard Lausen; Sheng Zha; George Karypis (AAAI, 2023)

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)

Differentially Private Optimization on Large Model at Small Cost. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2023)

Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (NeurIPS, 2023)

HyTrel: Hypergraph-enhanced Tabular Data Representation Learning. - Pei Chen; Soumajyoti Sarkar; Leonard Lausen; Balasubramaniam Srinivasan; Sheng Zha; Ruihong Huang; George Karypis (NeurIPS, 2023)

Large Language Models of Code Fail at Completing Code with Potential Bugs. - Tuan Dinh; Jinman Zhao; Samson Tan; Renato Negrinho; Leonard Lausen; Sheng Zha; George Karypis (NeurIPS, 2023)

Coupling public and private gradient provably helps optimization. - Ruixuan Liu; Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

On the accuracy and efficiency of group-wise clipping in differentially private optimization. - Zhiqi Bu; Ruixuan Liu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

Zero redundancy distributed learning with differential privacy. - Zhiqi Bu; Justin Chiu; Ruixuan Liu; Sheng Zha; George Karypis (CoRR, 2023)

Extreme Miscalibration and the Illusion of Adversarial Robustness. - Vyas Raina; Samson Tan; Volkan Cevher; Aditya Rawal; Sheng Zha; George Karypis (ACL (1), 2024)

Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs. - Dingmin Wang; Jinman Zhao; Hengzhi Pei; Samson Tan; Sheng Zha (ACL (Findings), 2024)

DEM: Distribution Edited Model for Training with Mixed Data Distributions. - Dhananjay Ram; Aditya Rawal; Momchil Hardalov; Nikolaos Pappas 002; Sheng Zha (EMNLP, 2024)

Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning. - Soumajyoti Sarkar; Leonard Lausen; Volkan Cevher; Thomas Brox; Sheng Zha; George Karypis (ENLSP, 2024)

Differentially Private Bias-Term Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2024)

Pre-training Differentially Private Models with Limited Public Data. - Zhiqi Bu; Xinwei Zhang 001; Sheng Zha; Mingyi Hong 001; George Karypis (NeurIPS, 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)