activity
20152017
most citedA Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model

4 citations · 4 across the 3 of their papers we have counts for

collaborators

7 papers

cs.GT2017

Selling to a No-Regret Buyer

Mark Braverman, Jieming Mao, Jon Schneider +1

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution in every…

cs.GT2017

Combinatorial Assortment Optimization

Nicole Immorlica, Brendan Lucier, Jieming Mao +2

Assortment optimization refers to the problem of designing a slate of products to offer potential customers, such as stocking the shelves in a convenience store. The price of each…

cs.DS20174 cited

A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model

Xi Chen, Yuanzhi Li, Jieming Mao

We study the active learning problem of top- ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top- items with high p…

cs.GT2017

Multi-armed Bandit Problems with Strategic Arms

Mark Braverman, Jieming Mao, Jon Schneider +1

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm…

cs.DS2016

Competitive analysis of the top-K ranking problem

Xi Chen, Sivakanth Gopi, Jieming Mao +1

Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top items from noisy pairwise c…

cs.DS2016

Parallel Algorithms for Select and Partition with Noisy Comparisons

Mark Braverman, Jieming Mao, S. Matthew Weinberg

We consider the problem of finding the highest element in a totally ordered set of elements (select), and partitioning a totally ordered set into the top and botto…