Online Discovery of Feature Dependencies
By Alborz Geramifard, Finale Doshi-Velez, Joshua D. Redding, Nicholas A. Roy, Jonathan P. How. 2011
64 citations recorded by the source corpus.
Subfields: Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Representation & Self-Supervised Learning
- Christoph Dann
- André Barreto
- Jan Peters
- David Silver
- Jonathan P. How
- Tom Schaul
- Amir-massoud Farahmand
- Ioannis Antonoglou
Citations in the corpus
- Prioritized Experience Replay (2015, methodology): “TD-errors have also been used as a prioritization mechanism for determining where to focus resources, for example when choosing where to explore (White et al., 2014) or which features to select (Geramifard et al., 2011; Sun et al., 2011).”
- RLPy: a value-function-based reinforcement learning framework for education and research (2015, methodology): “powerful framework and aim at improving existing methods by, for example, feature learning (Keller et al., 2006; Parr et al., 2007; Geramifard et al., 2011), or policies with better exploration-exploitation trade-off (Nouri and Littman, 2009; Jaksch et al.”
- Value-Aware Loss Function for Model-based Reinforcement Learning (2017, methodology): “…(e.g., [Silver et al., 2007; Liang et al., 2016]) or automatically generated data-dependently, e.g., [Petrik, 2007; Mahadevan and Maggioni, 2007; Parr et al., 2007; Mahadevan and Liu, 2010; Geramifard et al., 2011; Farahmand and Precup, 2012; B¨ohmer et al., 2013; Milani Fard et al., 2013].”
- Autonomous Agents and Multi-agent Systems Manuscript No. Learning Potential Functions and Their Representations for Multi-task Reinforcement Learning (unknown): “Both FS and state abstraction methods have been applied to single-task RL, with especially FS seeing a recent surge in interest [23,29,33,46,54,55].”
- Reinforcement learning with misspecified model classes (2013, methodology): “Even with the use of adaptive representations, modern model-free RL techniques tend to require many samples to achieve reasonable policies [2].”
- Policy evaluation with temporal differences: a survey and comparison (2015, methodology): “The approaches of Parr et al. (2007), Painter-Wakefield and Parr (2012a) and Geramifard et al. (2013, 2011) are based on the orthogonal matching principle (Pati et al., 1993) and incrementally add features which have high correlation with the temporal-difference error.”