1 code implementation • 25 Jul 2023 • Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini
Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from.
no code implementations • 13 Jan 2023 • Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
We find that DistDeD significantly improves over prior discovery approaches, providing indications of the risk 10 hours earlier on average as well as increasing detection by 20%.
1 code implementation • NeurIPS 2021 • Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning.
1 code implementation • 23 Nov 2020 • Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
no code implementations • 20 Jun 2020 • Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target domain may be both data-scarce and confounded.
no code implementations • 26 Nov 2018 • Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian, Sung-Hyun Son
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world.
1 code implementation • NeurIPS 2017 • Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.