1 citations · 2 across the 12 of their papers we have counts for
8 papers · 1 filter
Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
Riccardo Mereu, Aidan Scannell, Yuxin Hou +6
World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model C…
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…
Score-Based Density Estimation from Pairwise Comparisons
Petrus Mikkola, Luigi Acerbi, Arto Klami
We study density estimation from pairwise comparisons, motivated by expert knowledge elicitation and learning from human feedback. We relate the unobserved target density to a temp…
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Daolang Huang, Xinyi Wen, Ayush Bharti +2
Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instanta…
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Chengkun Li, Bobby Huggins, Petrus Mikkola +1
Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains. We propose normalizing flow regression (NFR), a…
Stacking Variational Bayesian Monte Carlo
Francesco Silvestrin, Chengkun Li, Luigi Acerbi
Approximate Bayesian inference for models with computationally expensive, black-box likelihoods poses a significant challenge, especially when the posterior distribution is complex…
Amortized Bayesian Experimental Design for Decision-Making
Daolang Huang, Yujia Guo, Luigi Acerbi +1
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 experime…