activity
20192021
most citedLearning to Price Against a Moving Target

1 citations · 1 across the 5 of their papers we have counts for

collaborators

9 papers

cs.GT2021

Pricing Query Complexity of Revenue Maximization

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1

The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most wit…

cs.GT2021

Online Allocation and Display Ads Optimization with Surplus Supply

Melika Abolhassani, Hossein Esfandiari, Yasamin Nazari +3

In this work, we study a scenario where a publisher seeks to maximize its total revenue across two sales channels: guaranteed contracts that promise to deliver a certain number of…

cs.GT20211 cited

Learning to Price Against a Moving Target

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1

In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood whe…

cs.GT2021

Pricing Ordered Items

Shuchi Chawla, Rojin Rezvan, Yifeng Teng +1

We study the revenue guarantees and approximability of item pricing. Recent work shows that with heterogeneous items, item-pricing guarantees an approximation to th…

cs.GT2020

Menu-size Complexity and Revenue Continuity of Buy-many Mechanisms

Shuchi Chawla, Yifeng Teng, Christos Tzamos

We study the multi-item mechanism design problem where a monopolist sells heterogeneous items to a single buyer. We focus on buy-many mechanisms, a natural class of mechanisms…

cs.GT2020

Why Do Competitive Markets Converge to First-Price Auctions?

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng

We consider a setting in which bidders participate in multiple auctions run by different sellers, and optimize their bids for the \emph{aggregate} auction. We analyze this setting…