1 citations · 2 across the 23 of their papers we have counts for
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Robust Experimental Design via Generalised Bayesian Inference
Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski
Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain…
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…
Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification
Roubing Tang, Sabina J. Sloman, Samuel Kaski
In many science and industry settings, a central challenge is designing experiments under time and budget constraints. Bayesian Optimal Experimental Design (BOED) is a paradigm to…
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…
PABBO: Preferential Amortized Black-Box Optimization
Xinyu Zhang, Daolang Huang, Samuel Kaski +1
Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical s…
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…
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis
Fabio S. Ferreira, John Ashburner, Arabella Bouzigues +33
In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and…
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…
Cost-aware simulation-based inference
Ayush Bharti, Daolang Huang, Samuel Kaski +1
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is t…