Applications of 0-1 Neural Networks in Prescription and Prediction

arXiv:2402.18851 · cs.LG, cs.AI, math.OC, stat.ML · Submitted 2024-02-29 · Read on arXiv

cs.LG, cs.AI, math.OC, stat.ML

Submitted: 2024-02-29

Updated: 2024-02-29

Code: https://github.com/microsoft/EconML

Terminology

• Regularization — 20× in this paper · explained in 3 episodes
• Doubly Robust — 19× in this paper
• Interpretability — 12× in this paper · explained in 15 episodes
• Loss Function — 10× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Activation Function — 8× in this paper · explained in 1 episode
• Causal Inference — 6× in this paper · explained in 4 episodes
• Objective Function — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Direct Method — 4× in this paper
• Exchangeability — 4× in this paper · explained in 2 episodes
• Few-Shot Learning — 4× in this paper · explained in 4 episodes
• Inverse Propensity Weighting — 4× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Prescriptive Neural Networks — 3× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• SHAP values — 3× in this paper · explained in 2 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Experiments on the Causal Forest — 1× in this paper
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
• Model Based Reinforcement Learning for Personalized Heparin Dosing
• Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
• Evaluating Robustness of Neural Networks with Mixed Integer Programming
• Solving Mixed Integer Programs Using Neural Networks
• Doubly Robust Policy Evaluation and Learning
• Learning Optimal Prescriptive Trees from Observational Data
• Strong Optimal Classification Trees

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