33 citations · 103 across the 20 of their papers we have counts for
24 papers
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…
Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
Yongkang Guo, Yuan Yuan, Jinshan Zhang +3
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomi…
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 …
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…
Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence
Paul Resnick, Yuqing Kong, Grant Schoenebeck +1
In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in such settings: (1) how to use hum…
Modularity and Mutual Information in Networks: Two Sides of the Same Coin
Qian Wang, Yongkang Guo, Zhihuan Huang +1
Modularity, first proposed by [Newman and Girvan, 2004], is one of the most popular ways to quantify the significance of community structure in complex networks. It can serve as bo…
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…
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…
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Xinwei Sun, Yilun Xu, Peng Cao +4
Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality w…
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…
L_DMI: An Information-theoretic Noise-robust Loss Function
Yilun Xu, Peng Cao, Yuqing Kong +1
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly d…
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong +1
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \em…
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…
Water from Two Rocks: Maximizing the Mutual Information
Yuqing Kong, Grant Schoenebeck
We build a natural connection between the learning problem, co-training, and forecast elicitation without verification (related to peer-prediction) and address them simultaneously…
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…