6 citations · 7 across the 2 of their papers we have counts for
5 papers · 1 filter
A Meta-Learning Approach to Bayesian Causal Discovery
Anish Dhir, Matthew Ashman, James Requeima +1
Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, su…
In-Context In-Context Learning with Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +1
Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each da…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil +2
Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (AD…
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt +3
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets. Such models aim to incorporate pre-specified, high-level concepts into the learning pr…