77 citations · 179 across the 54 of their papers we have counts for
6 papers · 1 filter
Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning
Luis Maßny, Christoph Hofmeister, Maximilian Egger +2
We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding re…
Equivalence of Insertion/Deletion Correcting Codes for -dimensional Arrays
Evagoras Stylianou, Lorenz Welter, Rawad Bitar +2
We consider the problem of correcting insertion and deletion errors in the -dimensional space. This problem is well understood for vectors (one-dimensional space) and was recent…
Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning
Serge Kas Hanna, Rawad Bitar, Parimal Parag +2
We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on workers, each having a subset of the data. Distributed SGD may…
Efficient Private Storage of Sparse Machine Learning Data
Marvin Xhemrishi, Maximilian Egger, Rawad Bitar
We consider the problem of maintaining sparsity in private distributed storage of confidential machine learning data. In many applications, e.g., face recognition, the data used in…
Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees
Marvin Xhemrishi, Rawad Bitar, Antonia Wachter-Zeh
We consider the problem of designing a coding scheme that allows both sparsity and privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy requi…
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +1
We consider the distributed SGD problem, where a main node distributes gradient calculations among workers. By assigning tasks to all the workers and waiting only for the f…