Cheng-Yu Hsieh

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Chih-Kuan YehGoogle BrainVerified email at google.com
Ranjay KrishnaAssistant Professor, University of WashingtonVerified email at cs.washington.edu
Alex RatnerAssistant Professor, University of WashingtonVerified email at cs.washington.edu
Chun-Liang LiResearch Scientist, AppleVerified email at apple.com
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Chen-Yu LeeResearch Scientist, GoogleVerified email at google.com
Tomas PfisterHead of AI Research @ Google CloudVerified email at google.com
David I. InouyeAssistant Professor, Purdue UniversityVerified email at purdue.edu
Arun Sai SuggalaGoogle ResearchVerified email at google.com
Jieyu ZhangUniversity of WashingtonVerified email at cs.washington.edu
Yasuhisa FujiiGoogle ResearchVerified email at google.com
Hsuan-Tien LinProfessor of Computer Science and Information Engineering, National Taiwan UniversityVerified email at csie.ntu.edu.tw
Chao ZhangAssistant Professor at College of Computing, Georgia TechVerified email at gatech.edu
Yue YuCollege of Computing, Georgia Institute of TechnologyVerified email at gatech.edu
Xuanqing LiuUniversity of California, Los AngelesVerified email at cs.ucla.edu
Cho-Jui HsiehUniversity of California, Los AngelesVerified email at cs.ucla.edu
Sanjiv KumarGoogle Fellow, VP, Google ResearchVerified email at google.com
Seungyeon KimGoogle ResearchVerified email at google.com
Aniruddha KembhaviSenior Director of Computer Vision, Allen Institute of Artificial IntelligenceVerified email at allenai.org
Zixian MaStanford University, University of WashingtonVerified email at cs.washington.edu

Cheng-Yu Hsieh

Ph.D. student, University of Washington

Verified email at cs.washington.edu - Homepage

Data-Centric AI Efficient Machine Learning Model Interpretability


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On the (In) fidelity and Sensitivity of Explanations CK Yeh, CY Hsieh, A Suggala, DI Inouye, PK Ravikumar Advances in Neural Information Processing Systems, 10965-10976, 2019	430	2019
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ... arXiv preprint arXiv:2305.02301, 2023	257	2023
A survey on programmatic weak supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022	84	2022
Evaluations and Methods for Explanation through Robustness Analysis CY Hsieh, CK Yeh, X Liu, P Ravikumar, S Kim, S Kumar, CJ Hsieh International Conference on Learning Representations, 2021	58	2021
Automatic bridge bidding using deep reinforcement learning CK Yeh, CY Hsieh, HT Lin IEEE Transactions on Games 10 (4), 365-377, 2018	53	2018
Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality CY Hsieh, J Zhang, Z Ma, A Kembhavi, R Krishna Advances in neural information processing systems 36, 2024	52	2024
Tool documentation enables zero-shot tool-usage with large language models CY Hsieh, SA Chen, CL Li, Y Fujii, A Ratner, CY Lee, R Krishna, T Pfister arXiv preprint arXiv:2308.00675, 2023	34	2023
How sensitive are sensitivity-based explanations CK Yeh, CY Hsieh, AS Suggala, D Inouye, P Ravikumar arXiv preprint arXiv:1901.09392, 52, 2019	24	2019
A deep model with local surrogate loss for general cost-sensitive multi-label learning CY Hsieh, YA Lin, HT Lin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018	17	2018
Nemo: Guiding and contextualizing weak supervision for interactive data programming CY Hsieh, J Zhang, A Ratner Proceedings of the VLDB Endowment 15 (13), 4093 - 4105, 2022	13	2022
Understanding Programmatic Weak Supervision via Source-aware Influence Function J Zhang, H Wang, CY Hsieh, A Ratner Advances in Neural Information Processing Systems, 2022	12	2022
A pseudo-label method for coarse-to-fine multi-label learning with limited supervision CY Hsieh, M Xu, G Niu, HT Lin, M Sugiyama Learning from Limited Labeled Data Workshop @ ICLR '19, 2019	9	2019
DataComp-LM: In search of the next generation of training sets for language models J Li, A Fang, G Smyrnis, M Ivgi, M Jordan, S Gadre, H Bansal, E Guha, ... arXiv preprint arXiv:2406.11794, 2024	2	2024
Active refinement for multi-label learning: a pseudo-label approach CY Hsieh, WI Lin, M Xu, G Niu, HT Lin, M Sugiyama arXiv preprint arXiv:2109.14676, 2021	1	2021
Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions YG Hsieh, CY Hsieh, SY Yeh, L Béthune, HP Ansari, PKA Vasu, CL Li, ... arXiv preprint arXiv:2407.06723, 2024		2024
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps YS Chuang, L Qiu, CY Hsieh, R Krishna, Y Kim, J Glass arXiv preprint arXiv:2407.07071, 2024		2024
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization CY Hsieh, YS Chuang, CL Li, Z Wang, LT Le, A Kumar, J Glass, A Ratner, ... arXiv preprint arXiv:2406.16008, 2024		2024
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better S Geng, CY Hsieh, V Ramanujan, M Wallingford, CL Li, PW Koh, ... arXiv preprint arXiv:2406.05184, 2024		2024
運用局部代理損失函數之深度模型於廣泛成本導向多標籤學習 CY Hsieh 國立臺灣大學資訊工程學系學位論文 2018, 1-28, 2018		2018
The Hard Positive Truth about Vision-Language Compositionality A Kamath, CY Hsieh, KW Chang, R Krishna

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