activity
20162021
most citedMax-MIG: an Information Theoretic Approach for Joint Learning from Crowds

12 citations · 20 across the 9 of their papers we have counts for

collaborators

15 papers

cs.GT2021

Information Elicitation Meets Clustering

Yuqing Kong

In the setting where we want to aggregate people's subjective evaluations, plurality vote may be meaningless when a large amount of low-effort people always report "good" regardles…

cs.GT20211 cited

Truthful Information Elicitation from Hybrid Crowds

Qishen Han, Sikai Ruan, Yuqing Kong +3

Suppose a decision maker wants to predict weather tomorrow by eliciting and aggregating information from crowd. How can the decision maker incentivize the crowds to report their in…

cs.MA2021

SURPRISE! and When to Schedule It

Zhihuan Huang, Shengwei Xu, You Shan +4

Information flow measures, over the duration of a game, the audience's belief of who will win, and thus can reflect the amount of surprise in a game. To quantify the relationship b…

cs.GT2021

More Dominantly Truthful Multi-task Peer Prediction with a Finite Number of Tasks

Yuqing Kong

In the setting where we ask participants multiple similar possibly subjective multi-choice questions (e.g. Do you like Bulbasaur? Y/N; do you like Squirtle? Y/N), peer prediction a…

cs.GT2021

Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective

Jiale Chen, Yuqing Kong, Yuxuan Lu

In the setting where a group of agents is asked a single subjective multi-choice question (e.g. which one do you prefer? cat or dog?), we are interested in evaluating the quality o…

cs.CL20202 cited

A Topological Method for Comparing Document Semantics

Yuqi Kong, Fanchao Meng, Benjamin Carterette

Comparing document semantics is one of the toughest tasks in both Natural Language Processing and Information Retrieval. To date, on one hand, the tools for this task are still rar…