… introduce a concept of equivalence between machine learning algorithms. We define two
notions of algorithmic equivalence, namely, weak and strong equivalence. These notions are …

… how learning families of structures relates to learning classes of languages by mapping learning
tasks for structures to equivalent learning … learning, learning from informant, equivalence …

… More important, however, is the number of DAG elements contained in the equivalence
classes over which our algorithm searches; as we shall see in Section 6, this number can be …

… We propose to use the RCA algorithm, which is a simple and efficient algorithm for learning
… by RCA is the optimal dimensionality reduction algorithm under the same criterion. We then …

… On the other hand, traditional sequential online algorithms … with the efficiency of streaming
algorithms. Examples of these … data mining and machine learning algorithms on a distributed …

… Besides helping to explain some past initiatives, we believe the concept of value equivalence
may also give rise to theoretical and algorithmic innovations that leverage the insights …

… two algorithms which can be used to implement equivalence programs. … algorithm requires
more than linear time. We leave as an open problem to construct any equivalence algorithm …

… We study an algorithmic equivalence technique between … equivalent to continuous-time
mirror descent by Amid and Warmuth [4], but theory for the analogous discrete time algorithms is …

… The method is based on a vectorisation of the similarity between the objects and the
application of a machine learning algorithm (SVM, ANN, LogReg, Random Forests) to …

… We demonstrate that there are machine learning algorithms that can achieve success for …
time for training the algorithm. It also means that some algorithms are robust to the choice of …