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Luigi Acerbi

17 accepted papers

2026

Efficient Autoregressive Inference for Transformer Probabilistic Models

ICLR 2026poster

Transformer-based models for amortized probabilistic inference, such as neural processes, prior-fitted networks, and tabular foundation models, excel at single-pass *marginal* prediction. However, many real-world applications require coherent *joint distributions* that capture dependencies between p…

Cited by 0SourceScholar
2026

PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference

ICLR 2026poster

Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging modern generative methods like diffusion models to map observed data to model parameters or future predictions. These a…

Cited by 0SourceScholar
2025

ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition

NeurIPS 2025spotlight

Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instantaneously perform inference based upon it. While amortized methods for Bayesian inference and experimental design offer pa…

Cited by 0SourcecodeScholar
2025

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference

AISTATS 2025poster

Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are leading models for probabilistic meta-learning with a tractable objective. Often trained on synthetic data, these models…

Cited by 0SourceScholar
2024

Amortized Bayesian Experimental Design for Decision-Making

NeurIPS 2024poster

Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system…

2024

Improving robustness to corruptions with multiplicative weight perturbations

NeurIPS 2024spotlight

Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones. Incorporating specific corruptions into the data augmentation pipeline can improve robustness to those corruptions but may harm performance on clean images and other types of distortion. In this paper, we introduce a…

2024

Input-gradient space particle inference for neural network ensembles

ICLR 2024spotlight

Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based variational inference (ParVI) methods enhance diversity by formalizing a repulsion term based on a network similarity k…

2023

Learning Robust Statistics for Simulation-based Inference under Model Misspecification

NeurIPS 2023poster

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. However, such methods are known to yield untrustworthy and mislead…

2023

Practical Equivariances via Relational Conditional Neural Processes

NeurIPS 2023poster

Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatio-temporal modeling, Bayesian Optimization and continuous control…

2022

Parallel MCMC Without Embarrassing Failures

AISTATS 2022poster

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 in parallel on (sub)posteriors defined on data partitions. Then, a server combines local results. While efficient, this f…

2022

Tackling covariate shift with node-based Bayesian neural networks

ICML 2022oral

Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational complexity of large-scale architectures and datasets. Node-based BNN…

2020

Dynamic allocation of limited memory resources in reinforcement learning

NeurIPS 2020poster

Biological brains are inherently limited in their capacity to process and store information, but are nevertheless capable of solving complex tasks with apparent ease. Intelligent behavior is related to these limitations, since resource constraints drive the need to generalize and assign importance d…

2017

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

NeurIPS 2017poster

Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successful…