huawei-noah/HEBO

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2797 stars · 472 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A research monorepo from Huawei Noah's Ark Lab containing official implementations of Bayesian optimization, reinforcement learning, and generative model projects. Its flagship HEBO library is a heteroscedastic evolutionary Bayesian optimization package that won the NeurIPS 2020 Black-Box Optimisation Challenge.

Use cases

• optimize hyperparameters of machine learning models
• black-box optimization of expensive functions
• combinatorial and mixed-variable Bayesian optimization
• high-dimensional Bayesian optimization
• safe reinforcement learning experiments
• offline reinforcement learning research
• antibody design with Bayesian optimization

When to choose

• you need a proven, competition-winning Bayesian optimization library in Python
• you want research-grade implementations of recent BO and RL papers
• your optimization problem has mixed or combinatorial variables

When to avoid

• you need a permissively licensed dependency - the repo has no explicit license
• you need production-hardened, well-documented tooling rather than research code
• you want a general-purpose ML framework rather than an optimization library

Facets

library · maturity active

machine-learning reinforcement-learning benchmarking machine-learning artificial-intelligence python cross-platform bayesian-optimization hyperparameter-optimization black-box-optimization reinforcement-learning generative-models research-code algorithms research

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markdown · JSON · MCP: product_card(name="huawei-noah/HEBO")