activity
20202026
most citedMechanism Design for Multi-Party Machine Learning

15 citations · 22 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.GT2023

Fair Price Discrimination

Siddhartha Banerjee, Kamesh Munagala, Yiheng Shen +1

A seller is pricing identical copies of a good to a stream of unit-demand buyers. Each buyer has a value on the good as his private information. The seller only knows the empirical…

cs.GT2023

Fair Multiwinner Elections with Allocation Constraints

Ivan-Aleksandar Mavrov, Kamesh Munagala, Yiheng Shen

We consider the multiwinner election problem where the goal is to choose a committee of candidates given the voters' utility functions. We allow arbitrary additional constraint…

cs.GT2022★ 2 cited

Auditing for Core Stability in Participatory Budgeting

Kamesh Munagala, Yiheng Shen, Kangning Wang

We consider the participatory budgeting problem where each of voters specifies additive utilities over candidate projects with given sizes, and the goal is to choose a subs…

cs.GT2021

Approximate Core for Committee Selection via Multilinear Extension and Market Clearing

Kamesh Munagala, Yiheng Shen, Kangning Wang +1

Motivated by civic problems such as participatory budgeting and multiwinner elections, we consider the problem of public good allocation: Given a set of indivisible projects (or ca…

cs.GT2021★ 1 cited

Targeting Makes Sample Efficiency in Auction Design

Yihang Hu, Zhiyi Huang, Yiheng Shen +1

This paper introduces the targeted sampling model in optimal auction design. In this model, the seller may specify a quantile interval and sample from a buyer's prior restricted to…

cs.GT2020

New Complexity Results on Coalitional Manipulation of Borda

Yiheng Shen, Pingzhong Tang, Yuan Deng

The Borda voting rule is a positional scoring rule for candidates such that in each vote, the first candidate receives points, the second points and so on. The winn…

cs.GT2020★ 4 cited

Truthful Data Acquisition via Peer Prediction

Yiling Chen, Yiheng Shen, Shuran Zheng

We consider the problem of purchasing data for machine learning or statistical estimation. The data analyst has a budget to purchase datasets from multiple data providers. She does…