22 citations · 26 across the 2 of their papers we have counts for
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stat.ML2018
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim +2
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability…
stat.ML2016
Deep Successor Reinforcement Learning
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam +1
Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternat…