Putting Peer Prediction Under the Micro(economic)scope and Making Truth-Telling Focal. - Yuqing Kong; Katrina Ligett; Grant Schoenebeck (WINE, 2016)

A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling. - Yuqing Kong; Grant Schoenebeck (CoRR, 2016)

Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity. - Yuqing Kong; Grant Schoenebeck (ITCS, 2018)

Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case. - Yuqing Kong; Grant Schoenebeck (ITCS, 2018)

Water from Two Rocks: Maximizing the Mutual Information. - Yuqing Kong; Grant Schoenebeck (EC, 2018)

Eliciting Expertise without Verification. - Yuqing Kong; Grant Schoenebeck (EC, 2018)

Eliciting and Aggregating Information: An Information Theoretic Approach. - Yuqing Kong (2018)

f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition. - Biqiao Zhang; Yuqing Kong; Georg Essl; Emily Mower Provost (AAAI, 2019)

Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds. - Peng Cao 004; Yilun Xu; Yuqing Kong; Yizhou Wang 001 (ICLR (Poster), 2019)

L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise. - Yilun Xu; Peng Cao 004; Yuqing Kong; Yizhou Wang 001 (NeurIPS, 2019)

Outsourcing Computation: The Minimal Refereed Mechanism. - Yuqing Kong; Chris Peikert; Grant Schoenebeck; Biaoshuai Tao (WINE, 2019)

Securely Trading Unverifiable Information without Trust. - Yuqing Kong; Yiping Ma 004; Yifan Wu (CoRR, 2019)

L_DMI: An Information-theoretic Noise-robust Loss Function. - Yilun Xu; Peng Cao 004; Yuqing Kong; Yizhou Wang 001 (CoRR, 2019)

An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling. - Yuqing Kong; Grant Schoenebeck (ACM Trans. Economics and Comput., 2019)

Information Elicitation Mechanisms for Statistical Estimation. - Yuqing Kong; Grant Schoenebeck; Biaoshuai Tao; Fang-Yi Yu (AAAI, 2020)

TCGM: An Information-Theoretic Framework for Semi-supervised Multi-modality Learning. - Xinwei Sun 001; Yilun Xu; Peng Cao 004; Yuqing Kong; Lingjing Hu; Shanghang Zhang; Yizhou Wang 001 (ECCV (3), 2020)

Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks. - Yuqing Kong (SODA, 2020)

SURPRISE! and When to Schedule It. - Zhihuan Huang; Shengwei Xu; You Shan; Yuxuan Lu 001; Yuqing Kong; Tracy Xiao Liu; Grant Schoenebeck (IJCAI, 2021)

Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective. - Jiale Chen 003; Yuqing Kong; Yuxuan Lu 001 (CoRR, 2021)

Counting the Number of People That Are Less Clever Than You. - Yuqing Kong (CoRR, 2021)

Modularity and Mutual Information in Networks: Two Sides of the Same Coin. - Yongkang Guo; Zhihuan Huang; Yuqing Kong; Qian Wang 025 (CoRR, 2021)

Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels. - Paul Resnick; Yuqing Kong; Grant Schoenebeck; Tim Weninger (CoRR, 2021)

Truthful Information Elicitation from Hybrid Crowds. - Qishen Han; Sikai Ruan; Yuqing Kong; Ao Liu 001; Farhad Mohsin; Lirong Xia (CoRR, 2021)

Identifying Fast/Slow Thinking without Prior. - Yuqing Kong; Yunqi Li; Yubo Zhang; Zhihuan Huang; Jinzhao Wu (CoRR, 2021)

Information Elicitation Meets Clustering. - Yuqing Kong (CoRR, 2021)

More Dominantly Truthful Multi-Task Peer Prediction with a Finite Number of Tasks. - Yuqing Kong (ITCS, 2022)

Eliciting Thinking Hierarchy without a Prior. - Yuqing Kong; Yunqi Li; Yubo Zhang; Zhihuan Huang; Jinzhao Wu (NeurIPS, 2022)

BONUS! Maximizing Surprise. - Zhihuan Huang; Yuqing Kong; Tracy Xiao Liu; Grant Schoenebeck; Shengwei Xu (WWW, 2022)

Learning to Bid in Repeated First-Price Auctions with Budgets. - Qian Wang 025; Zongjun Yang; Xiaotie Deng; Yuqing Kong (ICML, 2023)

False Consensus, Information Theory, and Prediction Markets. - Yuqing Kong; Grant Schoenebeck (ITCS, 2023)

Calibrating "Cheap Signals" in Peer Review without a Prior. - Yuxuan Lu 001; Yuqing Kong (NeurIPS, 2023)

Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms. - Yongkang Guo; Yuan Yuan 016; Jinshan Zhang 001; Yuqing Kong; Zhihua Zhu; Zheng Cai (WWW, 2023)

Multistable Perception, False Consensus, and Information Complements. - Yuqing Kong (CoRR, 2023)

Learning against Non-credible Auctions. - Qian Wang 025; Xuanzhi Xia; Zongjun Yang; Xiaotie Deng; Yuqing Kong; Zhilin Zhang 003; Liang Wang 001; Chuan Yu; Jian Xu 015; Bo Zheng 007 (CoRR, 2023)

Eliciting Informative Text Evaluations with Large Language Models. - Yuxuan Lu 001; Shengwei Xu; Yichi Zhang 003; Yuqing Kong; Grant Schoenebeck (EC, 2024)

The Surprising Benefits of Base Rate Neglect in Robust Aggregation. - Yuqing Kong; Shu Wang; Ying Wang 094 (EC, 2024)

Robust Decision Aggregation with Second-order Information. - Yuqi Pan; Zhaohua Chen 001; Yuqing Kong (WWW, 2024)

Peer Expectation in Robust Forecast Aggregation: The Possibility/Impossibility. - Yuqing Kong (CoRR, 2024)

Robust Decision Aggregation with Adversarial Experts. - Yongkang Guo; Yuqing Kong (CoRR, 2024)

How Gold to Make the Golden Snitch: Designing the "Game Changer" in Esports. - Zhihuan Huang; Yuxuan Lu 001; Yongkang Guo; Yuqing Kong (CoRR, 2024)

The Surprising Benefits of Base Rate Neglect in Robust Aggregation. - Yuqing Kong; Shu Wang; Ying Wang (CoRR, 2024)

Dominantly Truthful Peer Prediction Mechanisms with a Finite Number of Tasks. - Yuqing Kong (J. ACM, 2024)

Benchmarking LLMs' Judgments with No Gold Standard. - Shengwei Xu; Yuxuan Lu 001; Grant Schoenebeck; Yuqing Kong (ICLR, 2025)

Algorithmic Robust Forecast Aggregation. - Yongkang Guo; Jason D. Hartline; Zhihuan Huang; Yuqing Kong; Anant Shah; Fang-Yi Yu (EC, 2025)

Learning against Non-credible Second-Price Auctions. - Qian Wang 025; Xuanzhi Xia; Zongjun Yang; Xiaotie Deng; Yuqing Kong; Zhilin Zhang 003; Liang Wang 001; Chuan Yu; Jian Xu 015; Bo Zheng 007 (WWW, 2025)

Robust Aggregation with Adversarial Experts. - Yongkang Guo; Yuqing Kong (WWW, 2025)

Mitigating the Participation Bias by Balancing Extreme Ratings. - Yongkang Guo; Yuqing Kong; Jialiang Liu (WWW, 2025)