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Found 9 papers, 3 papers with code
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, Go, and Google Sheets (under the name Simple ML for Sheets).
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Modeling Text with Decision Forests using Categorical-Set Splits
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.
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
Deep Lattice Networks and Partial Monotonic Functions
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.
Fast and Flexible Monotonic Functions with Ensembles of Lattices
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.
A Light Touch for Heavily Constrained SGD
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.
Monotonic Calibrated Interpolated Look-Up Tables
Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable.
