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1 code implementation • 7 Jul 2022 • Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi

TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.

no code implementations • 21 Sep 2020 • Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev, Petr Mikheev, Jan Pfeifer

We define a condition that is specific to categorical-set features -- defined as an unordered set of categorical variables -- and present an algorithm to learn it, thereby equipping decision forests with the ability to directly model text, albeit without preserving sequential order.

no code implementations • 29 Jul 2020 • Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-Bert

Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data.

1 code implementation • 30 Nov 2018 • Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf

We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.

no code implementations • NeurIPS 2017 • Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta

We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network.

no code implementations • NeurIPS 2016 • Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta

For many machine learning problems, there are some inputs that are known to be positively (or negatively) related to the output, and in such cases training the model to respect that monotonic relationship can provide regularization, and makes the model more interpretable.

no code implementations • 15 Dec 2015 • Andrew Cotter, Maya Gupta, Jan Pfeifer

Minimizing empirical risk subject to a set of constraints can be a useful strategy for learning restricted classes of functions, such as monotonic functions, submodular functions, classifiers that guarantee a certain class label for some subset of examples, etc.

no code implementations • 23 May 2015 • Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojtek Moczydlowski, Alex van Esbroeck

Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable.

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