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Found 9 papers, 4 papers with code
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing
Cycling stress assessment, which quantifies cyclists' perceived stress imposed by the built environment and motor traffics, increasingly informs cycling infrastructure planning and cycling route recommendation.
Machine Learning-Augmented Optimization of Large Bilevel and Two-stage Stochastic Programs: Application to Cycling Network Design
We prove bounds on the optimality gap of the generated leader decision as measured by the original objective that considers the full follower set.
Dynamic Control of Service Systems with Returns: Application to Design of Post-Discharge Hospital Readmission Prevention Programs
We study a control problem for queueing systems where customers may return for additional episodes of service after their initial service completion.
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
OpenKBP: The open-access knowledge-based planning grand challenge
The purpose of this work is to advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research.
The importance of evaluating the complete automated knowledge-based planning pipeline
We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans.
Knowledge-based automated planning with three-dimensional generative adversarial networks
Our pipeline consisted of a generative adversarial network (GAN) to predict dose from a CT image followed by two optimization models to learn objective function weights and generate fluence-based plans, respectively.
Medical Physics
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones.
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities.
