9 citations · 10 across the 4 of their papers we have counts for
4 papers
Escaping Saddle Points with Compressed SGD
Dmitrii Avdiukhin, Grigory Yaroslavtsev
Stochastic gradient descent (SGD) is a prevalent optimization technique for large-scale distributed machine learning. While SGD computation can be efficiently divided between multi…
"Bring Your Own Greedy"+Max: Near-Optimal -Approximations for Submodular Knapsack
Dmitrii Avdiukhin, Grigory Yaroslavtsev, Samson Zhou
The problem of selecting a small-size representative summary of a large dataset is a cornerstone of machine learning, optimization and data science. Motivated by applications to re…
Adversarially Robust Submodular Maximization under Knapsack Constraints
Dmitrii Avdiukhin, Slobodan Mitrović, Grigory Yaroslavtsev +1
We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed…
Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent
Dmitrii Avdiukhin, Sergey Pupyrev, Grigory Yaroslavtsev
Motivated by performance optimization of large-scale graph processing systems that distribute the graph across multiple machines, we consider the balanced graph partitioning proble…