908 citations · 2.4k across the 149 of their papers we have counts for
6 papers · 2 filters
Why Extension-Based Proofs Fail
Dan Alistarh, James Aspnes, Faith Ellen +2
We introduce extension-based proofs, a class of impossibility proofs that includes valency arguments. They are modelled as an interaction between a prover and a protocol. Using pro…
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli +5
Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guar…
The Transactional Conflict Problem
Dan Alistarh, Syed Kamran Haider, Raphael Kübler +1
The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts…
Distributionally Linearizable Data Structures
Dan Alistarh, Trevor Brown, Justin Kopinsky +2
Relaxed concurrent data structures have become increasingly popular, due to their scalability in graph processing and machine learning applications. Despite considerable interest,…
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Dan Alistarh, Christopher De Sa, Nikola Konstantinov
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neu…
SparCML: High-Performance Sparse Communication for Machine Learning
Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh +2
Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data paral…