246 citations · 640 across the 16 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
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.LG2016★ 7 cited
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
Ark Anderson, Kyle Shaffer, Artem Yankov +2
In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically r…