36 citations · 41 across the 5 of their papers we have counts for
7 papers
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
Farzan Farnia, William Wang, Subhro Das +1
Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs a…
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Nathan Fulton, Nathan Hunt, Nghia Hoang +1
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees. Over the past decade, techniques based on forma…
Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
Subhro Das, Prasanth Lade, Soundar Srinivasan
Most predictive models assume that training and test data are generated from a stationary process. However, this assumption does not hold true in practice. In this paper, we consid…
Learning Occupational Task-Shares Dynamics for the Future of Work
Subhro Das, Sebastian Steffen, Wyatt Clarke +3
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying tas…
Learning Patient Engagement in Care Management: Performance vs. Interpretability
Subhro Das, Chandramouli Maduri, Ching-Hua Chen +1
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrollin…