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Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
Suppose an online platform wants to compare a treatment and control poli...
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Optimal and Greedy Algorithms for Multi-Armed Bandits with Many Arms
We characterize Bayesian regret in a stochastic multi-armed bandit probl...
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Experimental Design in Two-Sided Platforms: An Analysis of Bias
We develop an analytical framework to study experimental design in two-s...
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Semi-parametric dynamic contextual pricing
We consider a canonical revenue maximization problem where customers arr...
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Designing Informative Rating Systems for Online Platforms: Evidence from Two Experiments
Platforms critically rely on rating systems to learn the quality of mark...
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Designing Optimal Binary Rating Systems
Modern online platforms rely on effective rating systems to learn about ...
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Optimal Testing in the Experiment-rich Regime
Motivated by the widespread adoption of large-scale A/B testing in indus...
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Learning with Abandonment
Consider a platform that wants to learn a personalized policy for each u...
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Bandit Learning with Positive Externalities
Many platforms are characterized by the fact that future user arrivals a...
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On Learning the cμ Rule in Single and Parallel Server Networks
We consider learning-based variants of the c μ rule for scheduling in si...
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On Learning the cμ Rule: Single and Multiserver Settings
We consider learning-based variants of the c μ rule -- a classic and wel...
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Matching While Learning
We consider the problem faced by a service platform that needs to match ...
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Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for re...
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