100 citations · 387 across the 36 of their papers we have counts for
7 papers · 1 filter
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, Adish Singla
Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In…
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur +3
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) c…
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla +3
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may deci…
Sum-Product Networks for Sequence Labeling
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and ou…
Variational Inference for Data-Efficient Model Learning in POMDPs
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer +1
Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world…
Differentiable Submodular Maximization
Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
We consider learning of submodular functions from data. These functions are important in machine learning and have a wide range of applications, e.g. data summarization, feature se…