4 accepted papers
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation…
Partitioning algorithms play a key role in many scientific and engineering disciplines. A partitioning algorithm divides a set into a number of disjoint subsets or partitions. Often, the quality of the resulted partitions is measured by the amount of impurity in each partition, the smaller impurity…
Set partitioning is a key component of many algorithms in machine learning, signal processing and communications. In general, the problem of finding a partition that minimizes a given impurity (loss function) is NP-hard. As such, there exists a wealth of literature on approximate algorithms and theo…
Partitioning algorithms play a key role in machine learning, signal processing, and communications. They are used in many well-known NP-hard problems such as k-means clustering and vector quantization. The goodness of a partition scheme is measured by a given impurity function over the resulted part…