activity
20052026
most citedAll Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

1.2k citations · 5k across the 152 of their papers we have counts for

collaborators

159 papers

cs.LG2026

Getting Motif-ated: Controllable AI Compositions from Injected Motif Prompts

Chao Peter Yang, Cynthia Rudin, Yue Jiang +2

Deep learning has transformed symbolic music generation by borrowing the training paradigms of large language models, with systems such as NotaGen now producing complete, stylistic…

cs.LG2026

Falling Trees: A Model Class for Interpretable Risk Prioritization

Varun Babbar, Zachery Boner, Margo Seltzer +1

Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule lists (FRLs), which are orde…

cs.LG2026

Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators

Han Zhang, Alexander Ogren, Cynthia Rudin +2

Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems. However, eigenvalue problems, in which an eigenparameter and o…

cs.LG2026

ArborEnum: Decision Tree Rashomon Sets over Continuous Features

Zakk Heile, Hayden McTavish, Margo Seltzer +1

The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, fe…

cs.LG2026

Do Judges Behave Like Algorithms?

Riya Manchanda, Eric Chen, Chloe Zhu +3

What if judges already behave like algorithms? As artificial intelligence and algorithms are deployed in many settings, including the judicial system, many have debated whether jud…

cs.LG2026

Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7

Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…