Ramesh Johari

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Stanford University

Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach

Suppose an online platform wants to compare a treatment and control poli...
06/10/2020 ∙ by Peter Glynn, et al. ∙ 0 ∙ share

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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...
02/24/2020 ∙ by Mohsen Bayati, et al. ∙ 0 ∙ share

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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...
02/13/2020 ∙ by Ramesh Johari, et al. ∙ 0 ∙ share

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Semi-parametric dynamic contextual pricing

We consider a canonical revenue maximization problem where customers arr...
01/07/2019 ∙ by Virag Shah, et al. ∙ 0 ∙ share

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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...
10/30/2018 ∙ by Nikhil Garg, et al. ∙ 0 ∙ share

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Designing Optimal Binary Rating Systems

Modern online platforms rely on effective rating systems to learn about ...
06/18/2018 ∙ by Nikhil Garg, et al. ∙ 0 ∙ share

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Optimal Testing in the Experiment-rich Regime

Motivated by the widespread adoption of large-scale A/B testing in indus...
05/30/2018 ∙ by Sven Schmit, et al. ∙ 0 ∙ share

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Learning with Abandonment

Consider a platform that wants to learn a personalized policy for each u...
02/23/2018 ∙ by Ramesh Johari, et al. ∙ 0 ∙ share

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Bandit Learning with Positive Externalities

Many platforms are characterized by the fact that future user arrivals a...
02/15/2018 ∙ by Virag Shah, et al. ∙ 0 ∙ share

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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...
02/02/2018 ∙ by Subhashini Krishnasamy, et al. ∙ 0 ∙ share

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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...
02/02/2018 ∙ by Subhashini Krishnasamy, et al. ∙ 0 ∙ share

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Matching While Learning

We consider the problem faced by a service platform that needs to match ...
03/15/2016 ∙ by Ramesh Johari, et al. ∙ 0 ∙ share

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Online Active Linear Regression via Thresholding

We consider the problem of online active learning to collect data for re...
02/09/2016 ∙ by Carlos Riquelme, et al. ∙ 0 ∙ share

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Ramesh Johari

Featured Co-authors

Sanjay Shakkottai

Jose Blanchet

Baosen Zhang

Carlos Riquelme

Nikhil Garg

Vijay Kamble

Mohsen Bayati

Peter Glynn

Virag Shah

Khashayar Khosravi

Nima Hamidi

Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach

Optimal and Greedy Algorithms for Multi-Armed Bandits with Many Arms

Experimental Design in Two-Sided Platforms: An Analysis of Bias

Semi-parametric dynamic contextual pricing

Designing Informative Rating Systems for Online Platforms: Evidence from Two Experiments

Designing Optimal Binary Rating Systems

Optimal Testing in the Experiment-rich Regime

Learning with Abandonment

Bandit Learning with Positive Externalities

On Learning the cμ Rule in Single and Parallel Server Networks

On Learning the cμ Rule: Single and Multiserver Settings

Matching While Learning

Online Active Linear Regression via Thresholding