1 citations · 1 across the 9 of their papers we have counts for
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CausalARC: Abstract Reasoning with Causal World Models
Jacqueline Maasch, John Kalantari, Kia Khezeli
On-the-fly reasoning often requires adaptation to novel problems under limited data and distribution shift. This work introduces CausalARC: an experimental testbed for AI reasoning…
Probabilistic Graphical Models: A Concise Tutorial
Jacqueline Maasch, Willie Neiswanger, Stefano Ermon +1
Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under unce…
Compositional Causal Reasoning Evaluation in Language Models
Jacqueline R. M. A. Maasch, Alihan Hüyük, Xinnuo Xu +2
Causal reasoning and compositional reasoning are two core aspirations in AI. Measuring the extent of these behaviors requires principled evaluation methods. We explore a unified pe…