157 citations · 179 across the 9 of their papers we have counts for
9 papers
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)…
Tackling covariate shift with node-based Bayesian neural networks
Trung Trinh, Markus Heinonen, Luigi Acerbi +1
Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weigh…
Parallel MCMC Without Embarrassing Failures
Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski +1
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run i…
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Luigi Acerbi, Wei Ji Ma
Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult…