| Evaluation methods for unsupervised word embeddings T Schnabel, I Labutov, D Mimno, T Joachims Proceedings of the 2015 conference on empirical methods in natural language …, 2015 | 737 | 2015 |
| Recommendations as treatments: Debiasing learning and evaluation T Schnabel, A Swaminathan, A Singh, N Chandak, T Joachims In Proceedings of The International Conference on Machine Learning (ICML), 2016 | 584 | 2016 |
| Unbiased learning-to-rank with biased feedback T Joachims, A Swaminathan, T Schnabel Proceedings of the tenth ACM international conference on web search and data …, 2017 | 499 | 2017 |
| SummaC: Re-visiting NLI-based models for inconsistency detection in summarization P Laban, T Schnabel, PN Bennett, MA Hearst Transactions of the Association for Computational Linguistics 10, 163-177, 2022 | 134 | 2022 |
| Deep generalized method of moments for instrumental variable analysis A Bennett, N Kallus, T Schnabel Advances in neural information processing systems 32, 2019 | 117 | 2019 |
| Flors: Fast and simple domain adaptation for part-of-speech tagging T Schnabel, H Schütze Transactions of the Association for Computational Linguistics 2, 15-26, 2014 | 73 | 2014 |
| Effective evaluation using logged bandit feedback from multiple loggers A Agarwal, S Basu, T Schnabel, T Joachims Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 64 | 2017 |
| Short-term satisfaction and long-term coverage: Understanding how users tolerate algorithmic exploration T Schnabel, PN Bennett, ST Dumais, T Joachims Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018 | 46 | 2018 |
| Using shortlists to support decision making and improve recommender system performance T Schnabel, PN Bennett, ST Dumais, T Joachims Proceedings of the 25th International Conference on World Wide Web, 987-997, 2016 | 46 | 2016 |
| Keep it simple: Unsupervised simplification of multi-paragraph text P Laban, T Schnabel, P Bennett, MA Hearst arXiv preprint arXiv:2107.03444, 2021 | 31 | 2021 |
| Unbiased comparative evaluation of ranking functions T Schnabel, A Swaminathan, PI Frazier, T Joachims Proceedings of the 2016 ACM International Conference on the Theory of …, 2016 | 27 | 2016 |
| Stable coactive learning via perturbation K Raman, T Joachims, P Shivaswamy, T Schnabel International conference on machine learning, 837-845, 2013 | 26 | 2013 |
| Online updating of word representations for part-of-speech tagging W Yin, T Schnabel, H Schütze arXiv preprint arXiv:1604.00502, 2016 | 18 | 2016 |
| Debiasing item-to-item recommendations with small annotated datasets T Schnabel, PN Bennett Proceedings of the 14th ACM Conference on Recommender Systems, 73-81, 2020 | 17 | 2020 |
| Improving recommender systems beyond the algorithm T Schnabel, PN Bennett, T Joachims arXiv preprint arXiv:1802.07578, 2018 | 17 | 2018 |
| The impact of more transparent interfaces on behavior in personalized recommendation T Schnabel, S Amershi, PN Bennett, P Bailey, T Joachims Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 15 | 2020 |
| Shaping feedback data in recommender systems with interventions based on information foraging theory T Schnabel, PN Bennett, T Joachims Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019 | 15 | 2019 |
| Towards robust cross-domain domain adaptation for part-of-speech tagging T Schnabel | 15 | 2013 |
| HINT: Integration Testing for AI-based features with Humans in the Loop QZ Chen, T Schnabel, B Nushi, S Amershi 27th International Conference on Intelligent User Interfaces, 549-565, 2022 | 13 | 2022 |
| Towards a better understanding of predict and count models SS Keerthi, T Schnabel, R Khanna arXiv preprint arXiv:1511.02024, 2015 | 9 | 2015 |
Thorsten JoachimsProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
