01이 칸의 논문
- Nonstationary Experimental Design Under Structured Trends
- Estimation Errors as Regret Lower Bounds for Linear Contextual Bandits
- Markovian Search with Ex Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring
- The Benefits of Delay to Online Decision Making
- Getting the Most Out of A/B Tests Using the Asymptotic Minimax-Regret Criteria
- A Sample Size Calculation for Training and Certifying Targeting Policies
- Last-Iterate Convergence in No-Regret Learning: Games with Reference Effects Under Logit Demand
- Deferred Acceptance with News Utility
- Managerial Insight and “Optimal” Algorithms
- On Greedy-Like Policies in Online Matching with Reusable Network Resources and Decaying Rewards
- Uncertain Search with Knowledge Transfer
- Learning Personalized Treatment Strategies with Predictive and Prognostic Covariates in Adaptive Clinical Trials
- Whence LASSO? A Rational Interpretation
- Redesigning VolunteerMatch’s Search Algorithm: Toward More Equitable Access to Volunteers
- Treatment Allocation with Strategic Agents
- Sequential Search with Acquisition Uncertainty
- Fair Exploration via Axiomatic Bargaining
- Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference
- Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective
- Online Algorithms for Matching Platforms with Multichannel Traffic
- Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference
- A Simple and Optimal Policy Design with Safety Against Heavy-Tailed Risk for Stochastic Bandits
- Online Learning and Decision Making Under Generalized Linear Model with High-Dimensional Data
- Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation
- Weak Signal Asymptotics for Sequentially Randomized Experiments
- Algorithmic Transparency with Strategic Users
- Online Assortment Optimization for Two-Sided Matching Platforms
- Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
- Diffusion Approximations for a Class of Sequential Experimentation Problems
02같은 주제, 다른 방법
- Learning Optimal Prescriptive Trees from Observational Data
- A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals
- 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
- Learning to Optimally Stop Diffusion Processes, with Financial Applications
- Transfer Learning, Cross Learning and Co-Learning with Operational Data Analytics (ODA)
- Fast and Simple Adaptive Elicitations
- Using Neural Networks to Guide Data-Driven Operational Decisions
- EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference with ML- Generated Variables
- Learning to Cover: Online Learning and Optimization with Irreversible Decisions