| Causal inference in natural language processing: Estimation, prediction, interpretation and beyond A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ... Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022 | 175 | 2022 |
| Automatically Neutralizing Subjective Bias in Text R Pryzant, RD Martinez, N Dass, S Kurohashi, D Jurafsky, D Yang Proceedings of the Association for the Advancement of Artificial …, 2019 | 146 | 2019 |
| Effective domain mixing for neural machine translation D Britz, Q Le, R Pryzant Proceedings of the Second Conference on Machine Translation, 118-126, 2017 | 127 | 2017 |
| Automatic Prompt Optimization with "Gradient Descent" and Beam Search R Pryzant, D Iter, J Li, YT Lee, C Zhu, M Zeng arXiv preprint arXiv:2305.03495, 2023 | 82 | 2023 |
| Deconfounded lexicon induction for interpretable social science R Pryzant, K Shen, D Jurafsky, S Wager Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 72 | 2018 |
| JESC: Japanese-English Subtitle Corpus R Pryzant, Y Chung, D Jurafsky, D Britz 11th edition of the Language Resources and Evaluation Conference (LREC), 2017 | 69 | 2017 |
| Predicting Sales from the Language of Product Descriptions R Pryzant, Y Chung, D Jurafsky Special Interest Group on Information Retrieval (SIGIR) eCommerce Workshop, 2017 | 55 | 2017 |
| Causal Effects of Linguistic Properties R Pryzant, D Card, D Jurafsky, V Veitch, D Sridhar North American Association for Computational Linguistics (NAACL), 2021 | 38 | 2021 |
| Monitoring ethiopian wheat fungus with satellite imagery and deep feature learning R Pryzant, S Ermon, D Lobell Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 37 | 2017 |
| i-code: An integrative and composable multimodal learning framework Z Yang, Y Fang, C Zhu, R Pryzant, D Chen, Y Shi, Y Xu, Y Qian, M Gao, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10880 …, 2023 | 28 | 2023 |
| Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style R Pryzant, K Sone, S Basu Conference on Empirical Methods in Natural Language Processing (EMNLP …, 2018 | 27 | 2018 |
| Escalated police stops of Black men are linguistically and psychologically distinct in their earliest moments EH Rho, M Harrington, Y Zhong, R Pryzant, NP Camp, D Jurafsky, ... Proceedings of the National Academy of Sciences 120 (23), e2216162120, 2023 | 11 | 2023 |
| Automatic Rule Induction for Efficient Semi-Supervised Learning R Pryzant, Z Yang, Y Xu, C Zhu, M Zeng Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022 | 11* | 2022 |
| The Prochlorococcus carbon dioxide-concentrating mechanism: evidence of carboxysome-associated heterogeneity CS Ting, KH Dusenbury, RA Pryzant, KW Higgins, CJ Pang, CE Black, ... Photosynthesis research 123, 45-60, 2015 | 11 | 2015 |
| Prompt engineering a prompt engineer Q Ye, M Axmed, R Pryzant, F Khani arXiv preprint arXiv:2311.05661, 2023 | 7 | 2023 |
| The shifted and the overlooked: a task-oriented investigation of user-gpt interactions S Ouyang, S Wang, Y Liu, M Zhong, Y Jiao, D Iter, R Pryzant, C Zhu, H Ji, ... arXiv preprint arXiv:2310.12418, 2023 | 6 | 2023 |
| In-context demonstration selection with cross entropy difference D Iter, R Pryzant, R Xu, S Wang, Y Liu, Y Xu, C Zhu arXiv preprint arXiv:2305.14726, 2023 | 4 | 2023 |
| APOLLO: A simple approach for adaptive pretraining of language models for logical reasoning S Sanyal, Y Xu, S Wang, Z Yang, R Pryzant, W Yu, C Zhu, X Ren arXiv preprint arXiv:2212.09282, 2022 | 4 | 2022 |
| Reinforcement Learning for Atari Breakout VP Berges, P Rao, R Pryzant Stanford University CS 221 Project Paper, 2016 | 4 | 2016 |
| i-code v2: An autoregressive generation framework over vision, language, and speech data Z Yang, M Khademi, Y Xu, R Pryzant, Y Fang, C Zhu, D Chen, Y Qian, ... arXiv preprint arXiv:2305.12311, 2023 | 2 | 2023 |
Chenguang ZhuHead of Zoom GenAI ScienceΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα zoom.us
