96 citations · 204 across the 12 of their papers we have counts for
13 papers
Self-Play Learning Without a Reward Metric
Dan Schmidt, Nick Moran, Jonathan S. Rosenfeld +2
The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward fun…
Monotone Learning with Rectified Wire Networks
Veit Elser, Dan Schmidt, Jonathan Yedidia
We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one ou…
Proactive Message Passing on Memory Factor Networks
Patrick Eschenfeldt, Dan Schmidt, Stark Draper +1
We introduce a new type of graphical model that we call a "memory factor network" (MFN). We show how to use MFNs to model the structure inherent in many types of data sets. We also…
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
Charles Mathy, Nate Derbinsky, José Bento +2
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a for…
Proximal operators for multi-agent path planning
José Bento, Nate Derbinsky, Charles Mathy +1
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms. Recently this approach was explored in Bento et…
A message-passing algorithm for multi-agent trajectory planning
Jose Bento, Nate Derbinsky, Javier Alonso-Mora +1
We describe a novel approach for computing collision-free \emph{global} trajectories for agents with specified initial and final configurations, based on an improved version of…