Algorithms
Directions in ML: Structured Models for Automated Machine Learning
Automated machine learning (AutoML) seeks algorithmic methods for finding the best machine learning pipeline and hyperparameters to fit a new dataset. The complexity of this problem is astounding: viewed as an optimization problem, it entails…
SCCL-Runtime
Synthesized Collective Communication Library (SCCL) is an algorithm synthesizer for GPU communication in machine learning workloads. SCCL-Runtime, which is what we are seeking the release approval for, takes a synthesized algorithm from SCCL and runs…
ChaCha for Online AutoML
In this work, we propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. This work is published in ICML 2021. Python notebook demo: https://github.com/microsoft/FLAML/blob/main/notebook/autovw.ipynb
How can generative adversarial networks learn real-life distributions easily
A Generative adversarial network, or GAN, is one of the most powerful machine learning models proposed by Goodfellow et al. (opens in new tab) for learning to generate samples from complicated real-world distributions. GANs have…
