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Sheng Zha
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2020 – today
- 2024
- [c16]Vyas Raina, Samson Tan, Volkan Cevher, Aditya Rawal, Sheng Zha, George Karypis:
Extreme Miscalibration and the Illusion of Adversarial Robustness. ACL (1) 2024: 2500-2525 - [c15]Dingmin Wang, Jinman Zhao, Hengzhi Pei, Samson Tan, Sheng Zha:
Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs. ACL (Findings) 2024: 15854-15868 - [c14]Dhananjay Ram, Aditya Rawal, Momchil Hardalov, Nikolaos Pappas, Sheng Zha:
DEM: Distribution Edited Model for Training with Mixed Data Distributions. EMNLP 2024: 19287-19301 - [c13]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term Fine-tuning of Foundation Models. ICML 2024 - [i24]Vyas Raina, Samson Tan, Volkan Cevher, Aditya Rawal, Sheng Zha, George Karypis:
Extreme Miscalibration and the Illusion of Adversarial Robustness. CoRR abs/2402.17509 (2024) - [i23]Zhiqi Bu, Xinwei Zhang, Mingyi Hong, Sheng Zha, George Karypis:
Pre-training Differentially Private Models with Limited Public Data. CoRR abs/2402.18752 (2024) - [i22]Dhananjay Ram, Aditya Rawal, Momchil Hardalov, Nikolaos Pappas, Sheng Zha:
DEM: Distribution Edited Model for Training with Mixed Data Distributions. CoRR abs/2406.15570 (2024) - [i21]Soumajyoti Sarkar, Leonard Lausen, Volkan Cevher, Sheng Zha, Thomas Brox, George Karypis:
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning. CoRR abs/2409.01483 (2024) - 2023
- [c12]Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis:
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion. AAAI 2023: 5230-5238 - [c11]Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao:
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. EMNLP (Findings) 2023: 2775-2786 - [c10]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. ICML 2023: 3192-3218 - [c9]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS 2023 - [c8]Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis:
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning. NeurIPS 2023 - [c7]Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis:
Large Language Models of Code Fail at Completing Code with Potential Bugs. NeurIPS 2023 - [i20]Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis:
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion. CoRR abs/2306.00381 (2023) - [i19]Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis:
Large Language Models of Code Fail at Completing Code with Potential Bugs. CoRR abs/2306.03438 (2023) - [i18]Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis:
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning. CoRR abs/2307.08623 (2023) - [i17]Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Coupling public and private gradient provably helps optimization. CoRR abs/2310.01304 (2023) - [i16]Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao:
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. CoRR abs/2310.12442 (2023) - [i15]Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis:
On the accuracy and efficiency of group-wise clipping in differentially private optimization. CoRR abs/2310.19215 (2023) - [i14]Zhiqi Bu, Justin Chiu, Ruixuan Liu, Sheng Zha, George Karypis:
Zero redundancy distributed learning with differential privacy. CoRR abs/2311.11822 (2023) - 2022
- [c6]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. ACL (1) 2022: 719-730 - [c5]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. NAACL-HLT 2022: 2542-2550 - [i13]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. CoRR abs/2204.11117 (2022) - [i12]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR abs/2206.07136 (2022) - [i11]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term only Fine-tuning of Foundation Models. CoRR abs/2210.00036 (2022) - [i10]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. CoRR abs/2210.00038 (2022) - [i9]Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta, Leonard Lausen, Sheng Zha, Saab Mansour:
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic. CoRR abs/2211.03966 (2022) - 2021
- [c4]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. SustaiNLP@EMNLP 2021: 119-133 - [i8]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. CoRR abs/2109.11105 (2021) - [i7]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. CoRR abs/2110.07814 (2021) - 2020
- [j1]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Mach. Learn. Res. 21: 23:1-23:7 (2020) - [i6]Shuai Zheng, Haibin Lin, Sheng Zha, Mu Li:
Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes. CoRR abs/2006.13484 (2020)
2010 – 2019
- 2019
- [c3]He He, Sheng Zha, Haohan Wang:
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. DeepLo@EMNLP-IJCNLP 2019: 132-142 - [c2]Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola:
Dive into Deep Learning for Natural Language Processing. EMNLP/IJCNLP (2) 2019 - [i5]Sheng Zha, Ziheng Jiang, Haibin Lin, Zhi Zhang:
Just-in-Time Dynamic-Batching. CoRR abs/1904.07421 (2019) - [i4]Haibin Lin, Hang Zhang, Yifei Ma, Tong He, Zhi Zhang, Sheng Zha, Mu Li:
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources. CoRR abs/1904.12043 (2019) - [i3]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. CoRR abs/1907.04433 (2019) - [i2]He He, Sheng Zha, Haohan Wang:
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. CoRR abs/1908.10763 (2019) - 2018
- [c1]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. ECCV (4) 2018: 158-175 - [i1]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. CoRR abs/1804.02088 (2018)
Coauthor Index
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last updated on 2024-11-15 19:35 CET by the dblp team
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