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 e...
Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a subcommunity of machine learning has focused on solving thi...
Matthias Feurer, Jost Tobias Springenberg, Frank H...
We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approa...
Sherry Shanshan Ruan, Gheorghe Comanici, Prakash P...
Matching and merging data from conflicting sources is the bread and butter of data integration, which drives search verticals, e-commerce comparison sites and cyber intelligence....
Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable generati...
Stephen Mussmann, John Moore, Joseph John Pfeiffer...
This paper overviews the background, goals, past achievements and future directions of our research that aims to build a multivariate conditional anomaly detection framework for t...
The n-gram model has been widely used to capture the local ordering of words, yet its exploding feature space often causes an estimation issue. This paper presents local context s...
Seungyeon Kim, Joonseok Lee, Guy Lebanon, Haesun P...
Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can sup...
This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a correspond...
Joel Veness, Marc G. Bellemare, Marcus Hutter, Alv...