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Markel Sanz Ausin
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2020 – today
- 2024
- [i4]Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin, Neel Kant, Kriti Aggarwal, Neha Manjunath, Debajyoti Datta, Zhengliang Liu, Jiayuan Ding, Sophia Busacca, Cezanne Bianco, Swapnil Sharma, Rae Lasko, Michelle Voisard, Sanchay Harneja, Darya Filippova, Gerry Meixiong, Kevin Cha, Amir Youssefi, Meyhaa Buvanesh, Howard Weingram, Sebastian Bierman-Lytle, Harpreet Singh Mangat, Kim Parikh, Saad Godil, Alex Miller:
Polaris: A Safety-focused LLM Constellation Architecture for Healthcare. CoRR abs/2403.13313 (2024) - [i3]Gerald Shen, Zhilin Wang, Olivier Delalleau, Jiaqi Zeng, Yi Dong, Daniel Egert, Shengyang Sun, Jimmy J. Zhang, Sahil Jain, Ali Taghibakhshi, Markel Sanz Ausin, Ashwath Aithal, Oleksii Kuchaiev:
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment. CoRR abs/2405.01481 (2024) - 2023
- [j2]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way. Int. J. Artif. Intell. Educ. 33(4): 1031-1056 (2023) - [c12]Markel Sanz Ausin, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics. AIED (Posters/Late Breaking Results/...) 2023: 599-605 - [c11]Ge Gao, Song Ju, Markel Sanz Ausin, Min Chi:
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare. AAMAS 2023: 1504-1513 - [i2]Ge Gao, Song Ju, Markel Sanz Ausin, Min Chi:
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare. CoRR abs/2302.09212 (2023) - 2022
- [j1]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. Int. J. Artif. Intell. Educ. 32(2): 454-500 (2022) - 2021
- [c10]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. AIED (1) 2021: 356-368 - [c9]Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo-Jin Kim, Min Chi:
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. IEEE BigData 2021: 1337-1348 - [c8]Song Ju, Yeo Jin Kim, Markel Sanz Ausin, Maria E. Mayorga, Min Chi:
To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning. IEEE BigData 2021: 1640-1646 - [c7]Yeo Jin Kim, Markel Sanz Ausin, Min Chi:
Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention. IEEE BigData 2021: 1657-1663 - [i1]Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo-Jin Kim, Min Chi:
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. CoRR abs/2105.00568 (2021) - 2020
- [c6]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. AIED (1) 2020: 472-485 - [c5]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract). IJCAI 2020: 4691-4695
2010 – 2019
- 2019
- [c4]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Hierarchical Reinforcement Learning for Pedagogical Policy Induction. AIED (1) 2019: 544-556 - [c3]Markel Sanz Ausin, Hamoon Azizsoltani, Tiffany Barnes, Min Chi:
Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System. EDM 2019 - [c2]Hamoon Azizsoltani, Yeo-Jin Kim, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts. IJCAI 2019: 1974-1980 - 2018
- [c1]Shitian Shen, Markel Sanz Ausin, Behrooz Mostafavi, Min Chi:
Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. UMAP 2018: 43-51
Coauthor Index
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last updated on 2024-07-10 21:29 CEST by the dblp team
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