온라인 학습 및 최적화

• A Machine Learning Approach to Improving Dynamic Decision Making
  ISR · 2014 · 인용 134 · Georg Meyer, Gediminas Adomavičius, Paul Johnson 외
• Decision-Centric Active Learning of Binary-Outcome Models
  ISR · 2007 · 인용 64 · Maytal Saar‐Tsechansky, Foster Provost
• Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
  JMIS · 2008 · 인용 46 · Gaurav Bansal, Atish P. Sinha, Huimin Zhao
• Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach
  ISR · 2023 · 인용 37 · Tongxin Zhou, Yingfei Wang, Lu Yan 외
• Research Note—Discriminant Analysis with Strategically Manipulated Data
  ISR · 2014 · 인용 8 · Juheng Zhang, Haldun Aytuğ, Gary J. Kœhler
• A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation
  ISR · 2022 · 인용 10 · Carlos Fernández-Loría, Foster Provost, Jesse Anderton 외
• Data Mining with Combined Use of Optimization Techniques and Self-Organizing Maps for Improving Risk Grouping Rules: Application to Prostate Cancer Patients
  JMIS · 2005 · 인용 36 · Leonid Churilov, Adil Bagirov, Daniel Schwartz 외
• Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model
  ISR · 2023 · 인용 11 · Hongzhe Zhang, Xiaohang Zhao, Xiao Fang 외

2024–2026

• Calibration of Heterogeneous Treatment Effects in Randomized Experiments
  ISR · 2024 · 인용 5 · Yan Leng, Drew Dimmery
• A Robust Optimization Approach to Reliable Statistical Inference with Variables Generated by Machine Learning
  ISR · 2025 · 인용 0 · Aaron Schecter, Weifeng Li
• Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
  ISR · 2026 · 인용 0 · Hanyu Duan, Yi Yang, Ahmed Farhan Abbasi 외
• Fast Selection from Multiple Treatments: A Sequential Method for Principled Digital Experimentation
  ISR · 2026 · 인용 0 · Will Stamey, Ken Kelley, Bhargab Chattopadhyay 외

215편