Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Acerbi, Luigi"'
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 go
Externí odkaz:
http://arxiv.org/abs/2411.02064
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 objecti
Externí odkaz:
http://arxiv.org/abs/2410.15320
Eliciting a high-dimensional probability distribution from an expert via noisy judgments is notoriously challenging, yet useful for many applications, such as prior elicitation and reward modeling. We introduce a method for eliciting the expert's bel
Externí odkaz:
http://arxiv.org/abs/2410.08710
Autor:
Schmitt, Marvin, Li, Chengkun, Vehtari, Aki, Acerbi, Luigi, Bürkner, Paul-Christian, Radev, Stefan T.
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve both speed and accuracy when perform
Externí odkaz:
http://arxiv.org/abs/2409.04332
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
Externí odkaz:
http://arxiv.org/abs/2406.16540
Autor:
Singh, Gurjeet Sangra, Acerbi, Luigi
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 algorithm designed to efficiently solve difficult optimization problems
Externí odkaz:
http://arxiv.org/abs/2306.15576
Autor:
Huang, Daolang, Haussmann, Manuel, Remes, Ulpu, John, ST, Clarté, Grégoire, Luck, Kevin Sebastian, Kaski, Samuel, Acerbi, Luigi
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 mode
Externí odkaz:
http://arxiv.org/abs/2306.10915
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 formalizin
Externí odkaz:
http://arxiv.org/abs/2306.02775
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 met
Externí odkaz:
http://arxiv.org/abs/2305.15871
Publikováno v:
The Journal of Open Source Software, 8(86), 2023, 5428
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). VBMC is an approximate inference method designed for efficient param
Externí odkaz:
http://arxiv.org/abs/2303.09519