19 citations · 26 across the 5 of their papers we have counts for
5 papers
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…
Mutual information for fitting deep nonlinear models
Jacob S. Hunter, Nathan O. Hodas
Deep nonlinear models pose a challenge for fitting parameters due to lack of knowledge of the hidden layer and the potentially non-affine relation of the initial and observed layer…
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…
Network Weirdness: Exploring the Origins of Network Paradoxes
Farshad Kooti, Nathan O. Hodas, Kristina Lerman
Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety…
The Quality of Oscillations in Overdamped Networks
Nathan O. Hodas
The second law of thermodynamics implies that no macroscopic system may oscillate indefinitely without consuming energy. The question of the number of possible oscillations and the…