Search Results for author:
Found 16 papers, 13 papers with code
Amortized Bayesian Workflow (Extended Abstract)
Bayesian inference often faces a trade-off between computational speed and sampling accuracy.
Improving robustness to corruptions with multiplicative weight perturbations
Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones.
PyBADS: Fast and robust black-box optimization in Python
PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS) algorithm for fast and robust black-box optimization (Acerbi and Ma 2017).
Input-gradient space particle inference for neural network ensembles
To sidestep these difficulties, we propose First-order Repulsive Deep Ensemble (FoRDE), an ensemble learning method based on ParVI, which performs repulsion in the space of first-order input gradients.
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
PyVBMC: Efficient Bayesian inference in Python
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).
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
We propose the framework of post-process Bayesian inference as a means to obtain a quick posterior approximation from existing target density evaluations, with no further model calls.
Online simulator-based experimental design for cognitive model selection
In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.
Tackling covariate shift with node-based Bayesian neural networks
In this paper, we interpret these latent noise variables as implicit representations of simple and domain-agnostic data perturbations during training, producing BNNs that perform well under covariate shift due to input corruptions.
Parallel MCMC Without Embarrassing Failures
While efficient, this framework is very sensitive to the quality of subposterior sampling.
Dynamic allocation of limited memory resources in reinforcement learning
We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards.
Neurons and Cognition
Variational Bayesian Monte Carlo with Noisy Likelihoods
Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods.
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling
We provide theoretical arguments in favor of IBS and an empirical assessment of the method for maximum-likelihood estimation with simulation-based models.
Variational Bayesian Monte Carlo
We introduce here a novel sample-efficient inference framework, Variational Bayesian Monte Carlo (VBMC).
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation.
A Framework for Testing Identifiability of Bayesian Models of Perception
Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data.
