On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin Welcome back to AI Pioneers - in-depth conversations with those shaping the field . This time, we speak with Cynthia Rudin, a trailblazer in the field of interpretable machine learning. Winner of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, Cynthia's algorithms are already predicting seizures, aiding crime detection, and powering biological research . We discuss black boxes, Rashomon sets, and what's next for her lab - from cancer detection to interpretable AI-generated music. Can you tell me a bit about your background - what drew you into the field of interpretable machine learning?

GPUs have significantly accelerated first-order methods for large-scale optimization, especially in continuous optimization. However, this success has not transferred cleanly to problems with discrete variables, combinatorial structure, and nonlinear objectives, such as certifying optimal solutions for cardinality-constrained generalized linear models. Major challenges include the sequential processing of heterogeneous nodes in branch and bound (BnB) and frequent data movement between the CPU and GPU. We propose a simple, generic, and modular CPU--GPU framework that processes multiple BnB nodes in batches on GPUs. The framework is built around a small set of GPU-efficient routines and uses padding together with lightweight custom kernels to handle irregular node data structures. Experiments show one to two orders of magnitude speedups and zero optimality gap on challenging instances. The framework can also be extended to collect the entire Rashomon set, enabling downstream statistical analysis such as variable-importance analysis and model selection under secondary user-specific measures (e.g., AUC in classification).

Noise in data significantly influences decision-making in the data science process. In fact, it has been shown that noise in data generation processes leads practitioners to find simpler models. However, an open question still remains: what is the degree of model simplification we can expect under different noise levels? In this work, we address this question by investigating the relationship between the amount of noise and model simplicity across various hypothesis spaces, focusing on decision trees and linear models. We formally show that noise acts as an implicit regularizer for several different noise models. Furthermore, we prove that Rashomon sets (sets of near-optimal models) constructed with noisy data tend to contain simpler models than corresponding Rashomon sets with non-noisy data. Additionally, we show that noise expands the set of "good" features and consequently enlarges the set of models that use at least one good feature. Our work offers theoretical guarantees and practical insights for practitioners and policymakers on whether simple-yet-accurate machine learning models are likely to exist, based on knowledge of noise levels in the data generation process.

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The AUCsonthetrainingand are 0.855 and 0.859, respectively.

Rashomon sets can be large in size and complicated in structure, particularly for highly nonlinear function classes that allow complexinteraction terms, such asdecision trees.

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