Search Results for author:
Found 7 papers, 4 papers with code
Uncovering mesa-optimization algorithms in Transformers
Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood.
Evaluating the Moral Beliefs Encoded in LLMs
(2) We apply this method to study what moral beliefs are encoded in different LLMs, especially in ambiguous cases where the right choice is not obvious.
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Existing causal discovery methods typically require the data to be available in a centralized location.
On the Generalization and Adaption Performance of Causal Models
Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization.
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.
