Interpretable Policies for Dynamic Product Recommendations

By Marek Petrik, Ronny Luss. 2016

20 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. Scott Niekum
2. Daniel S. Brown
3. Amit Dhurandhar
4. Marek Petrik
5. Vijay Iyengar
6. Ronny Luss
7. Karthikeyan Shanmugam
8. Homa Alemzadeh

Citations in the corpus

• On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “n applications with very large outcome spaces such as those employing reinforcement learning, it is shown that appropriate aggregation of states in outcome policies can lead to 9 interpretable models [23]. 2) Safety Reserves: In machine learning formulations, the uncertainty in the matching of train”
• Bayesian Robust Optimization for Imitation Learning (2020, background): “In reinforcement learning, it has been shown that optimizing the regret is more appropriate if a good baseline policy is available [29, 31, 40].”
• TIP: Typifying the Interpretability of Procedures (2017, methodology): “Interpretable MDP: The authors used a constrained MDP formulation [41] to derive a product-to-product recommendation policy for the European tour operator TUI.”