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Planting trees on-chain - EZKL Blog

blog.ezkl.xyz · 1,355 words · saved by 1 readers

Full disclosure this post is not about gardening but about implementing ZK versions of machine learning algorithms with botanical nomenclature: decision trees, gradient boosted trees, and random forests. If you’re a keen gardener check this out. Lingering Github issues give us heart palpitations, particularly those that have been open for months on end. Sitting like mildew in an otherwise pristine home. Here’s one we’ve had open since January of this year: EZKL (for those not in the know), is a library for converting common computational graphs, in the (quasi)-universal .onnx format, into zero knowledge (ZK) circuits. This allows, for example, for: Though our library has improved in its scope of supported models, including transformer-based models (see here for a writeup), GANs, and LSTMs; implementing Kaggle crushing models like random forests and gradient boosting trees has been challenging. Part of the issue stems from the way sklearn, xgboost, and lightgbm models are exported to .o

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