59 citations · 252 across the 25 of their papers we have counts for
7 papers · 1 filter
Online Learning for Traffic Routing under Unknown Preferences
Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan +2
In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results…
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Mohsen Bayati, Nima Hamidi, Ramesh Johari +1
We investigate a Bayesian -armed bandit problem in the \emph{many-armed} regime, where and represents the time horizon. Initially, and aligned with recent…
Semi-parametric dynamic contextual pricing
Virag Shah, Jose Blanchet, Ramesh Johari
Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual inf…
Designing Optimal Binary Rating Systems
Nikhil Garg, Ramesh Johari
Modern online platforms rely on effective rating systems to learn about items. We consider the optimal design of rating systems that collect binary feedback after transactions. We…
Bandit Learning with Positive Externalities
Virag Shah, Jose Blanchet, Ramesh Johari
In many platforms, user arrivals exhibit a self-reinforcing behavior: future user arrivals are likely to have preferences similar to users who were satisfied in the past. In other…
Matching while Learning
Ramesh Johari, Vijay Kamble, Yash Kanoria
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in th…
Clustered Bandits
Loc Bui, Ramesh Johari, Shie Mannor
We consider a multi-armed bandit setting that is inspired by real-world applications in e-commerce. In our setting, there are a few types of users, each with a specific response to…