2.3k citations · 2.4k across the 19 of their papers we have counts for
7 papers · 1 filter
Causal Distillation for Language Models
Zhengxuan Wu, Atticus Geiger, Josh Rozner +5
Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. The standard approach to distillation trains a student mo…
Inducing Causal Structure for Interpretable Neural Networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu +5
In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashio…
Is Causal Reasoning Harder than Probabilistic Reasoning?
Milan Mossé, Duligur Ibeling, Thomas Icard
Many tasks in statistical and causal inference can be construed as problems of \emph{entailment} in a suitable formal language. We ask whether those problems are more difficult, fr…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
A Topological Perspective on Causal Inference
Duligur Ibeling, Thomas Icard
This paper presents a topological learning-theoretic perspective on causal inference by introducing a series of topologies defined on general spaces of structural causal models (SC…
Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard +1
Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method ground…
Preferential Structures for Comparative Probabilistic Reasoning
Matthew Harrison-Trainor, Wesley H. Holliday, Thomas F. Icard
Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing u…