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On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication
The communication time of these algorithms follows a complex interplay between a) the algorithm's convergence properties, b) the compression scheme, and c) the transmission rate offered by the digital channel.
Learning Kolmogorov Models for Binary Random Variables
We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
A Unified Framework for Training Neural Networks
The lack of mathematical tractability of Deep Neural Networks (DNNs) has hindered progress towards having a unified convergence analysis of training algorithms, in the general setting.
