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Cheng-Yu Hsieh
Cheng-Yu Hsieh
Ph.D. student, University of Washington
Verified email at cs.washington.edu - Homepage
Title
Cited by
Cited by
Year
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
3952019
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
1712023
A survey on programmatic weak supervision
J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner
arXiv preprint arXiv:2202.05433, 2022
742022
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
512021
Automatic bridge bidding using deep reinforcement learning
CK Yeh, CY Hsieh, HT Lin
IEEE Transactions on Games 10 (4), 365-377, 2018
502018
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
322024
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
252023
How sensitive are sensitivity-based explanations
CK Yeh, CY Hsieh, AS Suggala, D Inouye, P Ravikumar
arXiv preprint arXiv:1901.09392, 52, 2019
232019
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
172018
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
122022
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
92019
Understanding Programmatic Weak Supervision via Source-aware Influence Function
J Zhang, H Wang, CY Hsieh, A Ratner
Advances in Neural Information Processing Systems, 2022
82022
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
12021
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
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