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Found 11 papers, 0 papers with code
Applications of 0-1 Neural Networks in Prescription and Prediction
A key challenge in medical decision making is learning treatment policies for patients with limited observational data.
An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions
In this paper, we consider this challenge of designing personalized weight loss interventions that use direct financial incentives to motivate weight loss while remaining within a budget.
Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules
Reliable real-world deployment of reinforcement learning (RL) methods requires a nuanced understanding of their strengths and weaknesses and how they compare to those of humans.
Planning a Community Approach to Diabetes Care in Low- and Middle-Income Countries Using Optimization
Yet, scalable models to design and implement CHW programs while accounting for screening, management, and patient enrollment decisions have not been proposed.
Model Based Reinforcement Learning for Personalized Heparin Dosing
In this paper we propose such a framework to address the challenge of optimizing personalized heparin doses.
The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning
Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear how task characteristics contribute to overall difficulty for the machine learner.
A Mixed-Integer Programming Approach to Training Dense Neural Networks
Artificial Neural Networks (ANNs) are prevalent machine learning models that are applied across various real-world classification tasks.
Hard Choices in Artificial Intelligence
In this paper, we examine the vagueness in debates about the safety and ethical behavior of AI systems.
Optimal Local Explainer Aggregation for Interpretable Prediction
In contrast to other heuristic methods, we use an integer optimization framework to combine local explainers into a near-global aggregate explainer.
Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments
As AI systems become prevalent in high stakes domains such as surveillance and healthcare, researchers now examine how to design and implement them in a safe manner.
Non-Stationary Bandits with Habituation and Recovery Dynamics
Many settings involve sequential decision-making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward of each action is initially unknown.
