Loading Now

Summary of Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models, by Goutham Rajendran et al.


Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models

by Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar

First submitted to arxiv on: 14 Feb 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This research paper proposes a novel framework for learning human-interpretable concepts from data. By relating two broad approaches to building intelligent machine learning systems – inherently interpretable models and highly-performant foundation models – the authors demonstrate how to formally define and recover such concepts from diverse data. The approach combines ideas from causal representation learning and foundation models, showcasing its utility through experiments on synthetic data and large language models.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper shows us a new way to make machine learning systems more understandable. We can either build systems that are easy to understand or build very good systems and then try to figure out how they work. The authors combine these two ideas to develop a method for learning concepts from data. They test their approach on fake data and large language models, showing it works well.

Keywords

* Artificial intelligence  * Machine learning  * Representation learning  * Synthetic data