15 new papers from UBC Computer Science at world’s premier machine learning conference
UBC researchers had 15 papers accepted at ICML 2026, including two Spotlight papers
UBC Computer Science researchers will be in Seoul, South Korea from July 6 - 11, 2026 for the 43rd International Conference on Machine Learning (ICML 2026). ICML is one of the leading conferences for machine learning researchers to gather and discuss the latest advances in the field.
With nearly 24,000 submissions to the conference this year, a total of 6,352 papers were accepted. This year, researchers from UBC’s Computer Science department had 15 papers accepted into the conference. Two noteworthy papers, one from Professor Margo Seltzer and one from Assistant Professor Evan Shelhamer, were named “Spotlight” papers and are in the top 2.2% of paper submissions.
In addition to the 15 papers, Professor Mark Schmidt will be giving a tutorial entitled, “Is numerical optimization theory irrelevant to machine learning practice in 2026?” Professor Kevin Leyton-Brown serves as a Social Chair for the conference.
The following papers were accepted at ICML 2026:
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Yixiao Wang, Hayden McTavish, Varun Babbar, Margo Seltzer, Cynthia Rudin
Efficient Rashomon Set Approximation for Decision Trees
Zakk Heile, Hayden McTavish, Varun Babbar, Margo Seltzer, Cynthia Rudin
Flatland: The Adventures of Gradient Descent with Large Step Sizes
Leonardo Galli, Curtis Fox, Wiebke Bartolomaeus, Mark Schmidt, Holger Rauhut
Pluralistic Leaderboards
Nika Haghtalab, Ariel Procaccia, Han Shao, Serena Wang, Kunhe Yang
Rashomon Sets of Falling Trees
Varun Babbar, Zachery Boner, Margo Seltzer, Cynthia Rudin
Spotlight Poster
Robust AI Evaluation through Maximal Lotteries
Hadi Khalaf, Serena Wang, Daniel Halpern, Itai Shapira, Flavio Calmon, Ariel Procaccia
Self-Soupervision: Cooking Model Soups without Labels
Anthony Fuller, James Green, Evan Shelhamer
Spotlight Poster
Towards Parameter-Free Temporal Difference Learning
Yunxiang LI, Mark Schmidt, Reza Babanezhad, Sharan Vaswani