In traditional multi-instance (MI) learning, a single positive instance in a bag produces a positive class label. Hence, the learner knows how the bag’s class label depends on th...
Most work on intelligent information agents has thus far focused on systems that are accessible through the World Wide Web. As demanding schedules prohibit people from continuous ...
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
We revisit recently proposed algorithms for probabilistic clustering with pair-wise constraints between data points. We evaluate and compare existing techniques in terms of robust...
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...