Most machine learning algorithms share the following drawback: they only output bare predictions but not the con dence in those predictions. In the 1960s algorithmic information t...
We present a unified optimal semi-online algorithm for preemptive scheduling on uniformly related machines with the objective to minimize the makespan. This algorithm works for a...
We combine the work of Garg and K¨onemann, and Fleischer with ideas from dynamic graph algorithms to obtain faster (1 − ε)-approximation schemes for various versions of the mu...
An algorithm is presented that enables fast deformation of volumetric objects. Using this algorithm, rigid, deformable, elastic and plastic materials can be modeled by adjusting d...
In this paper, a hybrid algorithm based on the Multiple Offspring Sampling framework is presented and benchmarked on the BBOB-2010 noisy testbed. MOS allows the seamless combinat...