Research
ML Applications
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
December 7, 2020
Abstract
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate decision. Existing nonmyopic approaches are mostly heuristic and/or computationally expensive. In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree. Instead of solving these problems in a nested way, we equivalently optimize all decision variables in the full tree jointly, in a “one- shot” fashion. Combining this with an efficient method for implementing multi- step Gaussian process “fantasization,” we demonstrate that multi-step expected improvement is computationally tractable and exhibits performance superior to existing methods on a wide range of benchmarks.
AUTHORS
Written by
Shali Jiang
Daniel R. Jiang
Maximilian Balandat
Brian Karrer
Jacob R. Gardner
Roman Garnett
Research Topics
Machine learning
Related Publications
May 12, 2026
Human & Machine Intelligence
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
February 27, 2026
Human & Machine Intelligence
Unified Vision–Language Modeling via Concept Space Alignment
Yifu Qiu, Holger Schwenk, Paul-Ambroise Duquenne
February 27, 2026
February 11, 2026
Computer Vision
UniT: Unified Multimodal Chain-of-Thought Test-time Scaling
Leon Liangyu Chen, Haoyu Ma, Ziqi Huang, Xiaoliang Dai, Jialiang Wang, Zecheng He, Jianwei Yang, Chunyuan Li, Serena Yeung-Levy, Animesh Sinha, Chu Wang, Felix Juefei-Xu, Junzhe Sun, Zhipeng Fan
February 11, 2026
December 18, 2025
Computer Vision
Pixel Seal: Adversarial-only training for invisible image and video watermarking
Alexandre Mourachko, Hady Elsahar, Pierre Fernandez, Sylvestre Rebuffi, Tom Sander, Tomáš Souček, Tuan Tran, Valeriu Lacatusu
December 18, 2025
June 11, 2019
Computer Vision
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
June 11, 2019
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
October 10, 2016
Speech & Audio
Computer Vision
Polysemous Codes | Facebook AI Research
Matthijs Douze, Hervé Jégou, Florent Perronnin
October 10, 2016
June 18, 2018
Speech & Audio
Computer Vision
Low-shot learning with large-scale diffusion | Facebook AI Research
Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
June 18, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026