161 citations · 1k across the 69 of their papers we have counts for
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Linear Causal Disentanglement via Interventions
Chandler Squires, Anna Seigal, Salil Bhate +1
Causal disentanglement seeks a representation of data involving latent variables that relate to one another via a causal model. A representation is identifiable if both the latent…
Transfer Learning with Kernel Methods
Adityanarayanan Radhakrishnan, Max Ruiz Luyten, Neha Prasad +1
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine lear…
Active Learning for Optimal Intervention Design in Causal Models
Jiaqi Zhang, Louis Cammarata, Chandler Squires +2
Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When t…
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
Chandler Squires, Annie Yun, Eshaan Nichani +2
We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. We define latent factor causal models (LFCMs)…
Causal Structure Learning: a Combinatorial Perspective
Chandler Squires, Caroline Uhler
In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic gra…
Wide and Deep Neural Networks Achieve Optimality for Classification
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standa…