Cover Image

NUBO: A Transparent Python Package for Bayesian Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Diessner, Mike, Wilson, Kevin J., Whalley, Richard D.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Mathematical Software
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions
by: Diessner, Mike, et al.
Published: (2024)

scikit-fda: A Python Package for Functional Data Analysis
by: Ramos-Carreño, Carlos, et al.
Published: (2022)

MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
by: Zhou, Youran, et al.
Published: (2025)

hyppo: A Multivariate Hypothesis Testing Python Package
by: Panda, Sambit, et al.
Published: (2019)

Torch-Choice: A PyTorch Package for Large-Scale Choice Modeling with Python
by: Du, Tianyu, et al.
Published: (2023)

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
by: Hajij, Mustafa, et al.
Published: (2024)

evomap: A Toolbox for Dynamic Mapping in Python
by: Matthe, Maximilian
Published: (2025)

Jaya R Package -- A Parameter-Free Solution for Advanced Single and Multi-Objective Optimization
by: Bokde, Neeraj Dhanraj
Published: (2024)

tvopt: A Python Framework for Time-Varying Optimization
by: Bastianello, Nicola
Published: (2020)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
by: Liu, Jiashuo, et al.
Published: (2025)

EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python
by: Ziebell, Eric, et al.
Published: (2025)

TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
by: Cheng, Sibo, et al.
Published: (2024)

Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics
by: Diessner, Mike, et al.
Published: (2022)

BlackJAX: Composable Bayesian inference in JAX
by: Cabezas, Alberto, et al.
Published: (2024)

Cooper: A Library for Constrained Optimization in Deep Learning
by: Gallego-Posada, Jose, et al.
Published: (2025)

CDOpt: A Python Package for a Class of Riemannian Optimization
by: Xiao, Nachuan, et al.
Published: (2022)

Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package in R and Python
by: Saraceno, Giovanni, et al.
Published: (2024)

Differentiable Parameter Optimization for DAEs with State-Dependent Events
by: Matei, Ion, et al.
Published: (2026)

Dimensional Peeking for Low-Variance Gradients in Zeroth-Order Discrete Optimization via Simulation
by: Andelfinger, Philipp, et al.
Published: (2026)

DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces
by: Valentin, Romeo, et al.
Published: (2025)

regAL: Python Package for Active Learning of Regression Problems
by: Surzhikova, Elizaveta, et al.
Published: (2024)

GlycoPy: A CasADi-based Python Framework for Hierarchical Modeling, Optimization, and Control of Bioprocesses
by: Ma, Yingjie, et al.
Published: (2026)

FDApy: a Python package for functional data
by: Golovkine, Steven
Published: (2021)

tensorflow-riemopt: A Library for Optimization on Riemannian Manifolds
by: Smirnov, Oleg
Published: (2021)

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
by: Goujaud, Baptiste, et al.
Published: (2022)

A method of using RSVD in residual calculation of LowBit GEMM
by: Gu, Hongyaoxing
Published: (2024)

PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs
by: Cole, David L, et al.
Published: (2024)

Learning to Optimize by Differentiable Programming
by: Tao, Liping, et al.
Published: (2026)

A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent
by: Yang, James, et al.
Published: (2024)

Efficient Solvers for SLOPE in R, Python, Julia, and C++
by: Larsson, Johan, et al.
Published: (2025)

Self-Scaled Broyden Family of Quasi-Newton Methods in JAX
by: Bioli, Ivan, et al.
Published: (2026)

auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
by: Subramonian, Arjun, et al.
Published: (2025)

CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
by: Chen, Guangyi, et al.
Published: (2025)

Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiation
by: Hill, Adrian, et al.
Published: (2025)

Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
by: Antunes, Benjamin, et al.
Published: (2024)

NNTile: a machine learning framework capable of training extremely large GPT language models on a single node
by: Mikhalev, Aleksandr, et al.
Published: (2025)

PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates
by: Schreiber, Janina, et al.
Published: (2024)

forester: A Tree-Based AutoML Tool in R
by: Ruczyński, Hubert, et al.
Published: (2024)

Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds
by: Linwu, Jun, et al.
Published: (2025)

Statistical Inference and Quality Measures of KV Cache Quantisations Inspired by TurboQuant
by: D'Alberto, Paolo
Published: (2026)