| "Why Should I Trust You?": Explaining the Predictions of Any Classifier MT Ribeiro, S Singh, C Guestrin Knowledge Discovery and Data Mining (KDD), 2016 | 21899 | 2016 |
| Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 2666 | 2018 |
| AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts T Shin, Y Razeghi, RL Logan IV, E Wallace, S Singh Empirical Methods in Natural Language Processing (EMNLP), 2020 | 1875 | 2020 |
| Model-Agnostic Interpretability of Machine Learning MT Ribeiro, S Singh, C Guestrin ICML 2016 Workshop on Human Interpretability in Machine Learning, 2016 | 1327 | 2016 |
| Calibrate before use: Improving few-shot performance of language models Z Zhao, E Wallace, S Feng, D Klein, S Singh International Conference on Machine Learning, 12697-12706, 2021 | 1303 | 2021 |
| Beyond Accuracy: Behavioral Testing of NLP Models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh arXiv preprint arXiv:2005.04118, 2020 | 1171 | 2020 |
| Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods D Slack, S Hilgard, E Jia, S Singh, H Lakkaraju Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 180-186, 2020 | 1042 | 2020 |
| Universal Adversarial Triggers for Attacking and Analyzing NLP E Wallace, S Feng, N Kandpal, M Gardner, S Singh Empirical Methods in Natural Language Processing (EMNLP), 2019 | 933 | 2019 |
| DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs D Dua, Y Wang, P Dasigi, G Stanovsky, S Singh, M Gardner Annual Conference of the North American Chapter of the Association for …, 2019 | 931 | 2019 |
| Knowledge enhanced contextual word representations ME Peters, M Neumann, RL Logan IV, R Schwartz, V Joshi, S Singh, ... arXiv preprint arXiv:1909.04164, 2019 | 870 | 2019 |
| Generating Natural Adversarial Examples Z Zhao, D Dua, S Singh International Conference on Learning Representations (ICLR), 2018 | 732 | 2018 |
| Semantically Equivalent Adversarial Rules for Debugging NLP Models MT Ribeiro, S Singh, C Guestrin Annual Meeting of the Association for Computational Linguistics (ACL), 2018 | 567 | 2018 |
| Evaluating models’ local decision boundaries via contrast sets M Gardner, Y Artzi, V Basmov, J Berant, B Bogin, S Chen, P Dasigi, D Dua, ... Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 492 | 2020 |
| Injecting logical background knowledge into embeddings for relation extraction T Rocktäschel, S Singh, S Riedel Proceedings of the 2015 Human Language Technology Conference of the North …, 2015 | 314 | 2015 |
| Do NLP Models Know Numbers? Probing Numeracy in Embeddings E Wallace, Y Wang, S Li, S Singh, M Gardner arXiv preprint arXiv:1909.07940, 2019 | 306 | 2019 |
| Factorie: Probabilistic programming via imperatively defined factor graphs A McCallum, K Schultz, S Singh Advances in Neural Information Processing Systems 22, 1249-1257, 2009 | 293 | 2009 |
| Entity linking via joint encoding of types, descriptions, and context N Gupta, S Singh, D Roth Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 276 | 2017 |
| Design challenges for entity linking X Ling, S Singh, DS Weld Transactions of the Association for Computational Linguistics 3, 315-328, 2015 | 265 | 2015 |
| Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling RL Logan IV, NF Liu, ME Peters, M Gardner, S Singh Proceedings of the 2019 Conference of the Association for Computational …, 2019 | 253 | 2019 |
| Impact of pretraining term frequencies on few-shot numerical reasoning Y Razeghi, RL Logan IV, M Gardner, S Singh Findings of the Association for Computational Linguistics: EMNLP 2022, 840-854, 2022 | 251* | 2022 |
Carlos GuestrinProfessor, Stanford UniversityVerified email at stanford.edu
