work

Peer-reviewed publications and preprints in reverse chronological order.

This page lists my research output, including peer-reviewed publications and unpublished preprints (labeled with a Preprint badge where applicable).

2026

  1. Preprint
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    Disentanglement Beyond Generative Models with Riemannian ICA
    Edmond Cunningham
    2026
    Preprint, under review
    HTML PDF
  2. EIML@ICML
    Stacking-Based Weighting for Large Language Bayes in M-Open Settings
    Anant Bhide, Akarsh Gupta, Edmond Cunningham, and 1 more author
    In 2nd Workshop on Epistemic Intelligence in Machine Learning, 2026
    Accepted, EIML@ICML 2026
  3. ICLR GRaM
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    Conformal Coordinate Frames for Disentanglement
    Edmond Cunningham
    2026
    Accepted, ICLR 2026 Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM)
    HTML

2025

  1. Preprint
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    On Autoregressive Time Series Generation using Flow-based Generative Models
    Edmond Cunningham, Madalina Fiterau, and Daniel Sheldon
    2025
    PDF

2022

  1. ICML
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    Principal Component Flows
    Edmond Cunningham, Adam D. Cobb, and Susmit Jha
    In Proceedings of the 39th International Conference on Machine Learning, 2022
    HTML

2021

  1. AISTATS
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    A Change of Variables Method For Rectangular Matrix-Vector Products
    Edmond Cunningham and Madalina Fiterau
    In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
    HTML

2020

  1. ICML Wksp
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    Normalizing Flows Across Dimensions
    Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, and 2 more authors
    In Second Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2020
    HTML

2018

  1. NeurIPS ML4H
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    Explainable Genetic Inheritance Pattern Prediction
    Edmond Cunningham, Dana Schlegel, and Andrew DeOrio
    In NeurIPS Workshop on Machine Learning for Health, 2018
    HTML

2017

  1. NSGC
    Inheritance Pattern Prediction: An Ophthalmic Model for Digital Pedigree Feature Extraction and Machine Learning
    Dana Schlegel, Edmond Cunningham, Xinghai Zhang, and 3 more authors
    2017
    National Society of Genetic Counselors Conference (NSGC)