A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning
By Lesia Semenova, Cynthia Rudin. 2019
108 citations recorded by the source corpus.
Subfields: Scaling & Learning Theory, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Scaling & Learning Theory
- Cynthia Rudin
- Alexandra Chouldechova
- Amanda Coston
- Ashesh Rambachan
- Flávio du Pin Calmon
- Lesia Semenova
- Berk Ustun
In Statistical Learning Theory
- Berk Ustun
- Chudi Zhong
- Alexandra Chouldechova
- Amanda Coston
- Ashesh Rambachan
- Cynthia Rudin
- Zhi Chen
- Haiyang Huang
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “This brings us to another way to measure the Rashomon set: The pattern Rashomon ratio (Semenova et al., 2019; Marx et al., 2020), which could potentially be used to handle some of these problems.”
- Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “ will not make this kind of Occham’s-Razor-style argument, in favor of a technical argument about function classes, and in particular, Rashomon Sets. The argument below is fleshed out more formally in [59]. This is related to (but different from) the notation of “flat minima,” for which a nice example”
- Forward stability and model path selection (2021, neutral): “Within the recent Rashomon literature are methods for decision making (Tulabandhula and Rudin 2013, 2014), identifying ranges of variable importance within a model class (Fisher et al. 2019), and measuring model class simplicity (Semenova and Rudin 2019).”
- Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, methodology): “When defined in this manner, the set of good models is also called the “Rashomon set” (Rudin, 2019; Fisher et al., 2019; Dong & Rudin, 2020; Semenova et al., 2020).”
- Predictive Multiplicity in Classification (2019, background): “Semenova and Rudin [44] propose a measure for the relative size of the set of good models, and use it to characterize prediction problems where simple models perform well.”