Algorithms
Edge Machine Learning (Edge ML)
Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One example of a ubiquitous real-world application where such models are desirable is resource-scarce devices…
Combinatorial Assortment Optimization
A Deep Learning Theory: Global minima and over-parameterization
One empirical finding in deep learning is that simple methods such as stochastic gradient descent (SGD) have a remarkable ability to fit training data. From a capacity perspective, this may not be surprising— modern neural…
Fast, accurate, stable and tiny – Breathing life into IoT devices with an innovative algorithmic approach
In the larger quest to make the Internet of Things (IoT) a reality for people everywhere, building devices that can be both ultrafunctional and beneficent isn’t a simple matter. Particularly in the arena of resource-constrained,…
Chasing convex bodies and other random topics with Dr. Sébastien Bubeck
Episode 53, December 5, 2018 – Dr. Sébastien Bubeck explains the difficulty of the multi-armed bandit problem in the context of a parameter- and data-rich online world. He also discusses a host of topics from…
Unlikely research area reveals surprising twist in non-smooth optimization
Modern machine learning is characterized by two key features: high-dimensional models and very large datasets. Each of these features presents its own unique challenges, from basic issues such as storing and accessing all of the…
