| Mirroring to build trust in digital assistants K Metcalf, BJ Theobald, G Weinberg, R Lee, IM Jonsson, R Webb, ... arXiv preprint arXiv:1904.01664, 2019 | 23 | 2019 |
| Large language models as generalizable policies for embodied tasks A Szot, M Schwarzer, H Agrawal, B Mazoure, R Metcalf, W Talbott, ... The Twelfth International Conference on Learning Representations, 2023 | 21 | 2023 |
| Modeling unsupervised event segmentation: learning event boundaries from prediction errors K Metcalf, D Leake Proceedings of the Annual Meeting of the Cognitive Science Society 39, 2017 | 10 | 2017 |
| Symbol guided hindsight priors for reward learning from human preferences M Verma, K Metcalf arXiv preprint arXiv:2210.09151, 2022 | 9 | 2022 |
| Fedembed: Personalized private federated learning A Silva, K Metcalf, N Apostoloff, BJ Theobald arXiv preprint arXiv:2202.09472, 2022 | 9 | 2022 |
| Classification of regulatory paragraphs by discourse structure, reference structure, and regulation type A Buabuchachart, K Metcalf, N Charness, L Morgenstern Legal Knowledge and Information Systems, 59-62, 2013 | 6 | 2013 |
| Hindsight PRIORs for Reward Learning from Human Preferences M Verma, K Metcalf arXiv preprint arXiv:2404.08828, 2024 | 5 | 2024 |
| Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards K Metcalf, M Sarabia, N Mackraz, BJ Theobald arXiv preprint arXiv:2402.17975, 2024 | 5 | 2024 |
| Embedded word representations for rich indexing: a case study for medical records K Metcalf, D Leake Case-Based Reasoning Research and Development: 26th International Conference …, 2018 | 4 | 2018 |
| Automated Methods for Extracting and Expanding Lists in Regulatory Text. A Buabuchachart, N Charness, K Metcalf, L Morgenstern DoCoPe@ JURIX, 2013 | 4 | 2013 |
| Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning K Metcalf, M Sarabia, BJ Theobald arXiv preprint arXiv:2211.06527, 2022 | 3 | 2022 |
| Towards a Perceptual Model for Estimating the Quality of Visual Speech, Mar Z Aldeneh, M Fedzechkina, S Seto, K Metcalf, M Sarabia, N Apostoloff, ... arXiv preprint arXiv:2203.10117, 2022 | 3 | 2022 |
| Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations. K Metcalf, D Leake IJCAI, 3144-3150, 2019 | 2 | 2019 |
| Learning sharing behaviors with arbitrary numbers of agents K Metcalf, BJ Theobald, N Apostoloff arXiv preprint arXiv:1812.04145, 2018 | 2 | 2018 |
| A computational method for extracting, representing, and predicting social closeness K Metcalf, D Leake ECAI 2016, 1176-1184, 2016 | 2 | 2016 |
| Automated identification of relative social status K Metcalf, D Leake Proceedings of the Third Annual Conference on Advances in Cognitive Systems …, 2015 | 2 | 2015 |
| Generating responses to user interaction data based on user interaction-styles BJ Theobald, NE Apostoloff, GL Weinberg, RY Webb, KE Metcalf US Patent 11,769,016, 2023 | | 2023 |
| On the Role of LIP Articulation in Visual Speech Perception Z Aldeneh, M Fedzechkina, S Seto, K Metcalf, M Sarabia, N Apostoloff, ... ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | | 2023 |
| Representing Textual and Temporal Concepts Through Learned Continuous-Space Vector Embeddings K Metcalf Indiana University, 2019 | | 2019 |
| Investigating Methods and Representations for Reasoning About Social Context and Relative Social Power K Metcalf, D Leake Modeling and Using Context: 9th International and Interdisciplinary …, 2015 | | 2015 |
