21 citations · 56 across the 6 of their papers we have counts for
4 papers · 1 filter
ESTA: An Esports Trajectory and Action Dataset
Peter Xenopoulos, Claudio Silva
Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models. However, data from conventional sports is often unsuit…
AlphaD3M: Machine Learning Pipeline Synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin +5
We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations…
Bandit Modeling of Map Selection in Counter-Strike: Global Offensive
Guido Petri, Michael H. Stanley, Alec B. Hon +3
Many esports use a pick and ban process to define the parameters of a match before it starts. In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps,…
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco +4
Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bay…