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Found 7 papers, 1 papers with code
Adaptive Optimization for Prediction with Missing Data
When training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then compute predictions.
Solving the Quadratic Assignment Problem using Deep Reinforcement Learning
The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven particularly challenging to solve: unlike other combinatorial problems like the traveling salesman problem (TSP), which can be solved to optimality for instances with hundreds or even thousands of locations using advanced integer programming techniques, no methods are known to exactly solve QAP instances of size greater than 30.
Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
Missing data is a common issue in real-world datasets.
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
We develop a framework for value-function-based deep reinforcement learning with a combinatorial action space, in which the action selection problem is explicitly formulated as a mixed-integer optimization problem.
From predictions to prescriptions: A data-driven response to COVID-19
Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
The Price of Interpretability
When quantitative models are used to support decision-making on complex and important topics, understanding a model's ``reasoning'' can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks.
Optimal Explanations of Linear Models
We propose a general optimization framework to create explanations for linear models.
