6 citations · 11 across the 8 of their papers we have counts for
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cs.LG2021
DNA: Dynamic Network Augmentation
Scott Mahan, Tim Doster, Henry Kvinge
In many classification problems, we want a classifier that is robust to a range of non-semantic transformations. For example, a human can identify a dog in a picture regardless of…
cs.LG2021
Reward-Free Attacks in Multi-Agent Reinforcement Learning
Ted Fujimoto, Timothy Doster, Adam Attarian +2
We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward. In this work, we are motivated by the scenario…
cs.LG2021
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions
Scott Mahan, Henry Kvinge, Tim Doster
Building invariance to non-meaningful transformations is essential to building efficient and generalizable machine learning models. In practice, the most common way to learn invari…