Quantifying Uncertainty in Batch Personalized Sequential Decision Making

By Vukosi Marivate, Jessica J. Chemali, Emma Brunskill, Michael L. Littman. 2014

14 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Taylor W. Killian
2. Kaixiang Lin
3. Finale Doshi-Velez
4. George Dimitri Konidaris
5. Zhuangdi Zhu
6. Shamim Nemati
7. Vukosi Marivate
8. Anil K. Jain

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “-trained model parameters from past tasks, or directly learning a mapping between past and target tasks, thus extending personalized care to groups of patients with similar diagnoses. Marivate et al. [148] highlighted the potential benefit of taking into account individual variability and data limita”
• Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV (2019, unknown): “[12] formalized a routine to accommodate multiple sources of uncertainty in batch RL methods to better evaluate the effectiveness of treatments across subpopulations of HIV patients.”
• Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes (2017, background): “Also, recent progress has been made to formalize routines to accommodate multiple sources of uncertainty in batch RL methods to better evaluate the effectiveness of treatments across subpopulations of patients (Marivate et al. 2014) .”
• Improved empirical methods in reinforcement-learning evaluation (2015, background): “Portions of this dissertation are based on work previously published or submitted for publication by the author (Marivate et al., 2014; Marivate and Littman, 2013).”
• Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes (2016, methodology): “Marivate et al. [15] formalized a routine to accommodate multiple sources of uncertainty in batch RL methods to better evaluate the effectiveness of treatments across a subpopulations of patients.”
• Transfer Learning in Deep Reinforcement Learning: A Survey (2020, background): “decision making [166], [167].”