📢ICML alert: In the afternoon poster session, I'll present our paper: "Contextures: Representations from Contexts".

Our central argument: "Representations are learned from the association between input 𝑋 and context variable 𝐴"

📍East: E-1708, July 15, 4.30-7pm

📜 openreview.net/pdf?id=4GZwFPz…

Happening soon at West #5005 #NeurIPS2024

Come for one, stay for two CRL posters :)

We establish the necessary and sufficient conditions on the level of interventions required. Then, we propose a learning algorithm and analyze its optimality (gap).

Joint work w/ @atajer.bsky.social, Dmitriy Katz, Dennis Wei, and Prasanna Sattigeri

But without knowing the multi-node intervention targets, how do we find the correct combinations of given score functions? The key is the dimension of newly constructed score difference images.

Main idea: Combinations of score functions of multi-node interventions => new interventions with desired properties, e.g. sparsity. Intuitively, with diverse multi-node interventions (e.g. in the figure), we can synthesize any desired intervention!