01이 칸의 논문
- Learning to Optimally Stop Diffusion Processes, with Financial Applications
- Using Neural Networks to Guide Data-Driven Operational Decisions
- EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference with ML- Generated Variables
- T ail -GAN: Learning to Simulate Tail Risk Scenarios
- Multitask Learning and Bandits via Robust Statistics
- Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
- Leveraging Expert Consistency to Improve Algorithmic Decision Support
- Deep Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence
- Representing Random Utility Choice Models with Neural Networks
- Data-Pooling Reinforcement Learning for Preventative Healthcare Intervention
- Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making
- Model-Free Nonstationary Reinforcement Learning: Near-Optimal Regret and Applications in Multiagent Reinforcement Learning and Inventory Control
- A Machine Learning Framework for Assessing Experts’ Decision Quality
- Learning to Optimize Contextually Constrained Problems for Real-Time Decision Generation
- Nonstationary Reinforcement Learning: The Blessing of (More) Optimism
- Machine Learning for Demand Estimation in Long Tail Markets
- Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
- Distributionally Robust Batch Contextual Bandits
- Bandits atop Reinforcement Learning: Tackling Online Inventory Models with Cyclic Demands
- Ensemble Experiments to Optimize Interventions Along the Customer Journey: A Reinforcement Learning Approach
- Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression
- Tiered Assortment: Optimization and Online Learning
- Stochastic Optimization Forests
- Deep Reinforcement Learning for Sequential Targeting
- Active Learning for Contextual Search with Binary Feedback
- Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations
- Prescriptive Analytics for Flexible Capacity Management
- A Consensus Algorithm for Linear Support Vector Machines
- Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges
- Learning Preferences with Side Information
02같은 주제, 다른 방법
- Learning Optimal Prescriptive Trees from Observational Data
- A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals
- Nonstationary Experimental Design Under Structured Trends
- Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
- Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Panel Experiments
- Transfer Learning, Cross Learning and Co-Learning with Operational Data Analytics (ODA)
- Estimation Errors as Regret Lower Bounds for Linear Contextual Bandits
- Markovian Search with Ex Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring
- Fast and Simple Adaptive Elicitations
- Learning to Cover: Online Learning and Optimization with Irreversible Decisions