| Hierarchical reinforcement learning for pedagogical policy induction G Zhou, H Azizsoltani, MS Ausin, T Barnes, M Chi Artificial Intelligence in Education: 20th International Conference, AIED …, 2019 | 42 | 2019 |
| Leveraging deep reinforcement learning for pedagogical policy induction in an intelligent tutoring system MS Ausin, H Azizsoltani, T Barnes, M Chi In: Proceedings of the 12th International Conference on Educational Data …, 2019 | 32 | 2019 |
| Exploring the impact of simple explanations and agency on batch deep reinforcement learning induced pedagogical policies M Sanz Ausin, M Maniktala, T Barnes, M Chi Artificial Intelligence in Education: 21st International Conference, AIED …, 2020 | 27 | 2020 |
| Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts. H Azizsoltani, YJ Kim, M Sanz Ausin, T Barnes, M Chi In Proceedings of the 28th International Joint Conference on Artificial …, 2019 | 26 | 2019 |
| Improving learning & reducing time: A constrained action-based reinforcement learning approach S Shen, MS Ausin, B Mostafavi, M Chi Proceedings of the 26th conference on user modeling, adaptation and …, 2018 | 24 | 2018 |
| Leveraging granularity: Hierarchical reinforcement learning for pedagogical policy induction G Zhou, H Azizsoltani, MS Ausin, T Barnes, M Chi International journal of artificial intelligence in education 32 (2), 454-500, 2022 | 9 | 2022 |
| Tackling the credit assignment problem in reinforcement learning-induced pedagogical policies with neural networks MS Ausin, M Maniktala, T Barnes, M Chi International Conference on Artificial Intelligence in Education, 356-368, 2021 | 9 | 2021 |
| Hope: Human-centric off-policy evaluation for e-learning and healthcare G Gao, S Ju, MS Ausin, M Chi arXiv preprint arXiv:2302.09212, 2023 | 8 | 2023 |
| Infernet for delayed reinforcement tasks: Addressing the temporal credit assignment problem MS Ausin, H Azizsoltani, S Ju, YJ Kim, M Chi 2021 IEEE International Conference on Big Data (Big Data), 1337-1348, 2021 | 8 | 2021 |
| To reduce healthcare workload: Identify critical sepsis progression moments through deep reinforcement learning S Ju, YJ Kim, MS Ausin, ME Mayorga, M Chi 2021 IEEE International Conference on Big Data (Big Data), 1640-1646, 2021 | 5 | 2021 |
| Data processing with streaming data MS Ausin, S Huang, GD Baulier US Patent 10,157,213, 2018 | 4 | 2018 |
| Multi-temporal abstraction with time-aware deep q-learning for septic shock prevention YJ Kim, MS Ausin, M Chi 2021 IEEE International Conference on Big Data (Big Data), 1657-1663, 2021 | 2 | 2021 |
| A Transfer Learning Framework for Human-Centric Deep Reinforcement Learning with Reward Engineering MS Ausin North Carolina State University, 2021 | 2 | 2021 |
| A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics MS Ausin, M Abdelshiheed, T Barnes, M Chi International Conference on Artificial Intelligence in Education, 599-605, 2023 | 1 | 2023 |
| The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way MS Ausin International journal of artificial intelligence in education, 2022 | 1 | 2022 |
| InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem M Sanz Ausin, H Azizsoltani, S Ju, YJ Kim, M Chi arXiv e-prints, arXiv: 2105.00568, 2021 | 1 | 2021 |
| Polaris: A Safety-focused LLM Constellation Architecture for Healthcare S Mukherjee, P Gamble, MS Ausin, N Kant, K Aggarwal, N Manjunath, ... arXiv preprint arXiv:2403.13313, 2024 | | 2024 |
Min ChiDepartment of Computer Science, North Carolina State UniversityVahvistettu sähköpostiosoite verkkotunnuksessa ncsu.edu
