| 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 | 495 | 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 | 375 | 2023 |
| 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 | 92 | 2024 |
| A survey on programmatic weak supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022 | 90 | 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 | 63 | 2021 |
| Automatic bridge bidding using deep reinforcement learning CK Yeh, CY Hsieh, HT Lin IEEE Transactions on Games 10 (4), 365-377, 2018 | 58 | 2018 |
| 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 | 55 | 2023 |
| Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity L Yin, Y Wu, Z Zhang, CY Hsieh, Y Wang, Y Jia, G Li, A Jaiswal, ... arXiv preprint arXiv:2310.05175, 2023 | 42 | 2023 |
| 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 | 26 | 2024 |
| 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 |
| Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G J Li, A Fang, G Smyrnis, M Ivgi, M Jordan, S Gadre, H Bansal, E Guha, ... Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, and Vaishaal Shankar …, 2024 | 21 | 2024 |
| 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 | 19 | 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 | 16 | 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 | 13 | 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 |
| 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 | 7 | 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 | 5 | 2024 |
| Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning A Bandari, L Yin, CY Hsieh, AK Jaiswal, T Chen, L Shen, R Krishna, S Liu arXiv preprint arXiv:2410.07461, 2024 | 1 | 2024 |
| The hard positive truth about vision-language compositionality A Kamath, CY Hsieh, KW Chang, R Krishna arXiv preprint arXiv:2409.17958, 2024 | 1 | 2024 |
| 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 | 1 | 2024 |
Ranjay KrishnaAssistant Professor, University of Washington在 cs.washington.edu 的电子邮件经过验证
