208 citations · 369 across the 38 of their papers we have counts for
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cs.AI2021★ 3 cited
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati +3
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, b…
cs.LG2021★ 1 cited
Multihop: Leveraging Complex Models to Learn Accurate Simple Models
Amit Dhurandhar, Tejaswini Pedapati
Knowledge transfer from a complex high performing model to a simpler and potentially low performing one in order to enhance its performance has been of great interest over the last…