8 citations · 10 across the 3 of their papers we have counts for
4 papers
Graph Learning with Loss-Guided Training
Eliav Buchnik, Edith Cohen
Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains…
Self-Similar Epochs: Value in Arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hassidim +1
Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that sub-epochs compris…
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity
Eliav Buchnik, Edith Cohen
Graph-based semi-supervised learning (SSL) algorithms predict labels for all nodes based on provided labels of a small set of seed nodes. Classic methods capture the graph structur…
Reverse Ranking by Graph Structure: Model and Scalable Algorithms
Eliav Buchnik, Edith Cohen
Distances in a network capture relations between nodes and are the basis of centrality, similarity, and influence measures. Often, however, the relevance of a node to a node $v…