Exchangeable Generative Models with Flow Scans.
C Bender*, K O’Connor*, Y Li, JJ Garcia, M Zaheer and J Oliva.
To appear at AAAI Conference on Artificial Intelligence (20.6% acceptance rate), 2020.
Short version to appear at NeurIPS 2019 Workshop on Sets and Partitions.

*denotes equal contribution

Technical Reports

  • Gaussian Measures on Hilbert Spaces
    • A short report giving an introduction to Gaussian measures and Hilbert spaces, mostly referring to Da Prato’s Stochastic Equations in Infinite Dimensions.
  • A Taste of Information Geometry
    • A short report giving an introduction to a really interesting field, Information Geometry, which uses tools from differential geometry to study statistical models.


  • Few-Shot Learning
    • A slideshow I put together that summarizes two interesting papers (Vinyals et al. Matching Networks for One-shot Learning and Snell et al. Prototypical Networks for Few-shot Learning) about few-shot learning.


  • EDG Notes
    • My notes for O’Neill’s Elementary Differential Geometry. Though “notes” is kind of a misnomer in this case. This is more a collection of definitions and theorems. I use this type of notes for memorization and practicing filling in the content and discussion in my head.
  • BDA Ch2-3 Notes
    • My notes for Gelman et al.’s Bayesian Data Analysis.
  • MLPR Ch13-14 Notes
    • My personal notes for Kevin Murphy’s Machine Learning: A Probabilistic Perspective. I hope to eventually cover the whole book, but I imagine that might take me some time!
  • ESL Ch2 Solutions
    • My personal solutions to exercises in Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. So far I only have them for Chapter 2, but I hope to add more soon.