Tobias Jung
Postdoctoral Alumni
Tobias is interested in optimization and optimal decision-making: in particular how to act optimally when individual decisions have uncertain outcomes and far-reaching consequences. Knowing that his own abilities in this area are rather limited, he focuses his research on how these problems can be solved automatically and computationally. His thesis (nominated for the GI National Dissertation Prize) describes a novel and highly sample efficient online algorithm for reinforcement learning that is specifically aimed at learning in high-dimensional continuous state spaces. Some of his other work includes multivariate time series prediction, sensor evolution and curiosity-driven learning.
Empowerment for Continuous Agent-Environment Systems 2011
Tobias Jung, Daniel Polani, and Peter Stone, Adaptive Behavior, Vol. 19, 1 (2011), pp. 16-39.
Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration 2010
Tobias Jung and Peter Stone, In Proceedings of the European Conference on Machine Learning, September 2010.
Connectivity-based Localization in Robot Networks 2009
Tobias Jung, Mazda Ahmadi, and Peter Stone, In International Workshop on Robotic Wireless Sensor Networks (IEEE DCOSS '09), June 2009.
Feature Selection for Value Function Approximation Using Bayesian Model Selection 2009
Tobias Jung and Peter Stone, In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
Formerly affiliated with Learning Agents