On combining machine learning with decision making

By Theja Tulabandhula, Cynthia Rudin. 2011

33 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Hamed Rahimian
2. Sanjay Mehrotra
3. Lesia Semenova
4. Cynthia Rudin
5. Jean-Daniel Zucker
6. Theja Tulabandhula

In Safety, Risk, Reliability and Quality

1. Lesia Semenova
2. Cynthia Rudin
3. Theja Tulabandhula

Citations in the corpus

• 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).”
• Robust Optimization using Machine Learning for Uncertainty Sets (2014, background): “In our previous work [Tulabandhula and Rudin, 2013, 2014] we considered statistical learning theory bounds also for cases when unlabeled points were available.”
• The fused lasso penalty for learning interpretable medical scoring systems (2017, background): “Machine learning is increasingly used in decision making, and problems on their intersection are an active field of research [24].”
• A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, background): “This is done by Tulabandhula and Rudin (2014b) for machine learning to determine uncertainty sets for decision making.”
• Distributionally Robust Optimization: A Review (2019, background): “Tulabandhula and Rudin [303] incorporate machine learning for the decision making.”
• A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning (2019, methodology): “[43] Theja Tulabandhula and Cynthia Rudin.”