A radically more scalable alternative to Bayesian Optimization

In plain words

This software provides a computational approach designed as a more scalable alternative to Bayesian Optimization, a technique commonly used for hyperparameter tuning and optimization problems. It is intended for researchers, data scientists, and machine learning practitioners who work with optimization tasks that require improved efficiency at scale. The project emphasizes scalability as its primary distinction, offering a different methodology than traditional Bayesian approaches for complex optimization challenges.

written from the facts on this page · September 2026

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  Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!

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  Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…

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