Stable Differentiable Causal Discovery

no code implementations • 17 Nov 2023 • Achille Nazaret, Justin Hong, Elham Azizi, David Blei

We find that SDCD outperforms existing methods in both convergence speed and accuracy and can scale to thousands of variables.

Causal Discovery

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data

no code implementations • 29 Aug 2023 • Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab

In drug discovery, mapping interactions between genes within cellular systems is a crucial early step.

Drug Discovery

Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)

7 code implementations • 28 Sep 2020 • Konstantinos, Katsiapis, Abhijit Karmarkar, Ahmet Altay, Aleksandr Zaks, Neoklis Polyzotis, Anusha Ramesh, Ben Mathes, Gautam Vasudevan, Irene Giannoumis, Jarek Wilkiewicz, Jiri Simsa, Justin Hong, Mitch Trott, Noé Lutz, Pavel A. Dournov, Robert Crowe, Sarah Sirajuddin, Tris Brian Warkentin, Zhitao Li

In closing, we will also share a glimpse into the future of TFX.

Robust Federated Learning in a Heterogeneous Environment

no code implementations • 16 Jun 2019 • Avishek Ghosh, Justin Hong, Dong Yin, Kannan Ramchandran

Then, leveraging the statistical model, we solve the robust heterogeneous Federated Learning problem \emph{optimally}; in particular our algorithm matches the lower bound on the estimation error in dimension and the number of data points.

Clustering Federated Learning