12 papers · 1 filter
Use What You Know: Causal Foundation Models with Partial Graphs
Arik Reuter, Anish Dhir, Cristiana Diaconu +6
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified a…
The Neural Tangent Kernel for Classification
Jonathan Plenk, Sergio Calvo-Ordonez, Alvaro Cartea +3
In wide neural networks, the Neural Tangent Kernel (NTK) remains approximately constant during training, providing a powerful theoretical tool for studying training dynamics, gener…
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu +3
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the ca…
Rethinking Aleatoric and Epistemic Uncertainty
Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope +3
The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discu…
PSyDUCK: Training-Free Steganography for Latent Diffusion
Aqib Mahfuz, Georgia Channing, Mark van der Wilk +3
Recent advances in generative AI have opened promising avenues for steganography, which can securely protect sensitive information for individuals operating in hostile environments…
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…