RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

By Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister. 2019

11 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. Bryan Lim
2. Sercan Ö. Arik
3. Tomas Pfister
4. Shoaib Ahmed Siddiqui
5. Nicolas Loeff
6. Sara Hooker
7. Tegan Maharaj
8. Nitarshan Rajkumar

Citations in the corpus

• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, methodology): “tion to determine the contribution time-varying inputs. Instance-wise Variable Importance with DNNs: Instance (i.e. sample)- wise variable importance can be obtained with post-hoc explanation methods [15, 16, 26] and inherently intepretable models [27, 24]. Post-hoc explanation methods, e.g. LIME [1”
• Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics (2022, background): “…corpus of data (Zhang, 1992; Bien and Tibshirani, 2012; Kim et al., 2015; Kim et al., 2016), which can be used to speed up training (Sener and Savarese, 2018; Shim et al., 2021; Huggins et al., 2017; Sorscher et al., 2022) or aid in interpretability of the model predictions (Yoon et al., 2019).”