157 citations · 179 across the 9 of their papers we have counts for
6 papers · 1 filter
PyBADS: Fast and robust black-box optimization in Python
Gurjeet Sangra Singh, Luigi Acerbi
PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS) algorithm for fast and robust black-box optimization (Acerbi and Ma 2017). BADS is an optimization a…
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes +5
Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification.…
Input-gradient space particle inference for neural network ensembles
Trung Trinh, Markus Heinonen, Luigi Acerbi +1
Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based…
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang, Ayush Bharti, Amauri Souza +2
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics t…
Online simulator-based experimental design for cognitive model selection
Alexander Aushev, Aini Putkonen, Gregoire Clarte +4
The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discrimina…
PyVBMC: Efficient Bayesian inference in Python
Bobby Huggins, Chengkun Li, Marlon Tobaben +2
PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020)…