33 citations · 103 across the 20 of their papers we have counts for
15 papers · 1 filter
Learning to Bid in Repeated First-Price Auctions with Budgets
Qian Wang, Zongjun Yang, Xiaotie Deng +1
Budget management strategies in repeated auctions have received growing attention in online advertising markets. However, previous work on budget management in online bidding mainl…
False Consensus, Information Theory, and Prediction Markets
Yuqing Kong, Grant Schoenebeck
We study a setting where Bayesian agents with a common prior have private information related to an event's outcome and sequentially make public announcements relating to their inf…
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…
Eliciting Thinking Hierarchy without a Prior
Yuqing Kong, Yunqi Li, Yubo Zhang +2
When we use the wisdom of the crowds, we usually rank the answers according to their popularity, especially when we cannot verify the answers. However, this can be very dangerous w…
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…
BONUS! Maximizing Surprise
Zhihuan Huang, Yuqing Kong, Tracy Xiao Liu +2
Multi-round competitions often double or triple the points awarded in the final round, calling it a bonus, to maximize spectators' excitement. In a two-player competition with …
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…
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…
Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks
Yuqing Kong
In the setting where participants are asked multiple similar possibly subjective multi-choice questions (e.g. Do you like Panda Express? Y/N; do you like Chick-fil-A? Y/N), a serie…
Outsourcing Computation: the Minimal Refereed Mechanism
Yuqing Kong, Chris Peikert, Grant Schoenebeck +1
We consider a setting where a verifier with limited computation power delegates a resource intensive computation task---which requires a computation tableau---to two pr…
Securely Trading Unverifiable Information without Trust
Yuqing Kong, Yiping Ma, Yifan Wu
In future, information may become one of the most important assets in economy. However, unlike common goods (e.g. clothing), information is troublesome in trading since the informa…
Eliciting Expertise without Verification
Yuqing Kong, Grant Schoenebeck
A central question of crowd-sourcing is how to elicit expertise from agents. This is even more difficult when answers cannot be directly verified. A key challenge is that sophistic…
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Yuqing Kong, Grant Schoenebeck
In the setting where information cannot be verified, we propose a simple yet powerful information theoretical framework---the Mutual Information Paradigm---for information elicitat…
Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
Yuqing Kong, Grant Schoenebeck
Peer-prediction is a mechanism which elicits privately-held, non-variable information from self-interested agents---formally, truth-telling is a strict Bayes Nash equilibrium of th…
Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal
Yuqing Kong, Grant Schoenebeck, Katrina Ligett
Peer-prediction is a (meta-)mechanism which, given any proper scoring rule, produces a mechanism to elicit privately-held, non-verifiable information from self-interested agents. F…