Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. - Shitian Shen; Markel Sanz Ausin; Behrooz Mostafavi; Min Chi (UMAP, 2018)

Hierarchical Reinforcement Learning for Pedagogical Policy Induction. - Guojing Zhou; Hamoon Azizsoltani; Markel Sanz Ausin; Tiffany Barnes; Min Chi (AIED (1), 2019)

Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System. - Markel Sanz Ausin; Hamoon Azizsoltani; Tiffany Barnes; Min Chi (EDM, 2019)

Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts. - Hamoon Azizsoltani; Yeo-Jin Kim; Markel Sanz Ausin; Tiffany Barnes; Min Chi (IJCAI, 2019)

Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. - Markel Sanz Ausin; Mehak Maniktala; Tiffany Barnes; Min Chi (AIED (1), 2020)

Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract). - Guojing Zhou; Hamoon Azizsoltani; Markel Sanz Ausin; Tiffany Barnes; Min Chi (IJCAI, 2020)

Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. - Markel Sanz Ausin; Mehak Maniktala; Tiffany Barnes; Min Chi (AIED (1), 2021)

InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. - Markel Sanz Ausin; Hamoon Azizsoltani; Song Ju; Yeo-Jin Kim; Min Chi (IEEE BigData, 2021)

To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning. - Song Ju; Yeo Jin Kim; Markel Sanz Ausin; Maria E. Mayorga; Min Chi (IEEE BigData, 2021)

Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention. - Yeo Jin Kim; Markel Sanz Ausin; Min Chi (IEEE BigData, 2021)

Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. - Guojing Zhou; Hamoon Azizsoltani; Markel Sanz Ausin; Tiffany Barnes; Min Chi (Int. J. Artif. Intell. Educ., 2022)

A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics. - Markel Sanz Ausin; Mark Abdelshiheed; Tiffany Barnes; Min Chi (AIED (Posters/Late Breaking Results/...), 2023)

HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare. - Ge Gao; Song Ju; Markel Sanz Ausin; Min Chi (AAMAS, 2023)

The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way. - Markel Sanz Ausin; Mehak Maniktala; Tiffany Barnes; Min Chi (Int. J. Artif. Intell. Educ., 2023)

More, May not the Better: Insights from Applying Deep Reinforcement Learning for Pedagogical Policy Induction. - Gyuhun Jung; Markel Sanz Ausin; Tiffany Barnes; Min Chi (EDM, 2024)

Polaris: A Safety-focused LLM Constellation Architecture for Healthcare. - 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; et al. (CoRR, 2024)

NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment. - 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; et al. (CoRR, 2024)