116 citations · 626 across the 37 of their papers we have counts for
4 papers · 1 filter
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
Wendelin Böhmer, Rong Guo, Klaus Obermayer
This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic pol…
Graph Quantization
Brijnesh J. Jain, Klaus Obermayer
Vector quantization(VQ) is a lossy data compression technique from signal processing, which is restricted to feature vectors and therefore inapplicable for combinatorial structures…
Elkan's k-Means for Graphs
Brijnesh J. Jain, Klaus Obermayer
This paper extends k-means algorithms from the Euclidean domain to the domain of graphs. To recompute the centroids, we apply subgradient methods for solving the optimization-based…
A Necessary and Sufficient Condition for Graph Matching Being Equivalent to the Maximum Weight Clique Problem
Brijnesh Jain, Klaus Obermayer
This paper formulates a necessary and sufficient condition for a generic graph matching problem to be equivalent to the maximum vertex and edge weight clique problem in a derived a…