mlsanityData to 7 Oct 2026

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization

arXiv 2207.13676 ↗First posted 27 Jul 2022cs.LG cs.DC stat.MLcited by 17

Not on the map. Why?

Abstract (arXiv)

Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 35 of its 82 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

35 of 82 references.

References · 35

  1. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  2. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  3. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  4. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
  5. 2019AutoML: A Survey of the State-of-the-Artcited by 388
  6. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  7. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 318
  8. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  9. 2021Dataset Distillation with Infinitely Wide Convolutional Networkscited by 215
  10. 2019A Survey on Distributed Machine Learningcited by 187
  11. 2020Finite Versus Infinite Neural Networks: an Empirical Studycited by 156
  12. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 148
23 more
  1. 2021Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learningcited by 123
  2. 2020Infinite attention: NNGP and NTK for deep attention networkscited by 117
  3. 2020Hyper-Parameter Optimization: A Review of Algorithms and Applicationscited by 114
  4. 2018Searching for Efficient Multi-Scale Architectures for Dense Image Predictioncited by 96
  5. 2017Neural Optimizer Search with Reinforcement Learningcited by 82
  6. 2018Learning Navigation Behaviors End-to-End with AutoRLcited by 70
  7. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  8. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 64
  9. 2021Evolving Reinforcement Learning Algorithmscited by 48
  10. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  11. 2017Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathycited by 42
  12. 2020Exact posterior distributions of wide Bayesian neural networkscited by 29
  13. 2019Evolving Rewards to Automate Reinforcement Learningcited by 28
  14. 2021Reverb: A Framework For Experience Replaycited by 27
  15. 2019Long-Range Indoor Navigation with PRM-RLcited by 24
  16. 2021Emulation of physical processes with Emukitcited by 24
  17. 2017Towards Practical Differential Privacy for SQL Queriescited by 19
  18. 2021PyGlove: Symbolic Programming for Automated Machine Learningcited by 19
  19. 2021A Full-Stack Search Technique for Domain Optimized Deep Learning Acceleratorscited by 19
  20. 2021Apollo: Transferable Architecture Explorationcited by 17
  21. 2019Learning a Multi-Domain Curriculum for Neural Machine Translationcited by 16
  22. 2020Learning to Prove from Synthetic Theoremscited by 13
  23. 2014RProtoBuf: Efficient Cross-Language Data Serialization in Rcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 5; 2025: 1; 2026: 2.

17 papers.

Sort:

By area:

  1. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  2. 2024OmniPred: Language Models as Universal Regressorscited by 34
  3. 2024Reinforced In-Context Black-Box Optimizationcited by 14
  4. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  5. 2024The Vizier Gaussian Process Bandit Algorithmcited by 14
  6. 2025OptunaHub: A Platform for Black-Box Optimizationcited by 14
  7. 2023Massively Scaling Heteroscedastic Classifierscited by 11
  8. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  9. 2022Differentially Private Image Classification from Featurescited by 7
  10. 2023Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagationcited by 7
  11. 2022Task Selection for AutoML System Evaluationcited by 6
  12. 2023Scalable End-to-End ML Platforms: from AutoML to Self-servecited by 2
5 more
  1. 2023Pareto optimal proxy metricscited by 2
  2. 2023Hyperparameter Optimization as a Service on INFN Cloudcited by 1
  3. 2024AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Designcited by 1
  4. 2026You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvementcited by 1
  5. 2026Backdoor Mitigation in Decentralized LLM Fine-Tuningcited by 0