21 citations · 21 across the 4 of their papers we have counts for
4 papers
PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference
Yang Yang, Severi Rissanen, Paul E. Chang +5
Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging…
Efficient Autoregressive Inference for Transformer Probabilistic Models
Conor Hassan, Nasrulloh Loka, Cen-You Li +6
Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tabular foundation models, excel at singl…
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
Paul E. Chang, Nasrulloh Loka, Daolang Huang +3
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 l…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…