Publications

Nirbhay Modhe, Ran Xiao, Matthew Clark, Cheng Ding, Duc Do, Randall Lee, Timothy Ruchti, Xiao Hu (2023). Time-Aware Deep Sequential Models for In-Hospital Code Blue Prediction using Monitor Alarms. Accepted as Extended Abstract In IEEE BHI 2023.

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Ran Xiao, Matthew Clark, Nirbhay Modhe, Cheng Ding, Delgersuren Bold, Timothy Ruchti, Xiao Hu (2023). Characterizing trending features in time-series prediction of clinical event onset. Accepted as Extended Abstract In IEEE BHI 2023.

Ran Xiao, Matthew Clark, Delgersuren Bold, Cheng Ding, Nirbhay Modhe, Timothy Ruchti, Xiao Hu (2023). Assessing the Generalizability of Pre-trained Predictive Models for Hemorrhage, Emergent Intubation, and Sepsis to Predict In-hospital Cardiac Arrest. In 2023 Computing in Cardiology (CinC).

Nirbhay Modhe, Qiaozi Gao, Dhruv Batra, Ashwin Kalyan, Govind Thattai, Gaurav Sukhatme (2022). Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations.

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Nirbhay Modhe, Harish Kamath, Dhruv Batra, Ashwin Kalyan (2021). Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practice.

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Nirbhay Modhe, Harish Kamath, Dhruv Batra, Ashwin Kalyan (2020). Bridging Worlds in Reinforcement Learning with Model-Advantage. In 4th Lifelong Machine Learning Workshop at ICML 2020.

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Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra (2020). IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL. In IJCAI 2020.

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Nirbhay Modhe, Vikas Jain, Piyush Rai (2017). Scalable Generative Models for Multi-label Learning with Missing Labels. In ICML 2017.

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