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Yuqing Kong
Yuqing Kong
Assistant professor, Peking University
Bestätigte E-Mail-Adresse bei pku.edu.cn - Startseite
Titel
Zitiert von
Zitiert von
Jahr
L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Y Xu, P Cao, Y Kong, Y Wang
Advances in neural information processing systems 32, 2019
1992019
An information theoretic framework for designing information elicitation mechanisms that reward truth-telling
Y Kong, G Schoenebeck
ACM Transactions on Economics and Computation (TEAC) 7 (1), 1-33, 2019
662019
Putting peer prediction under the micro (economic) scope and making truth-telling focal
Y Kong, K Ligett, G Schoenebeck
Web and Internet Economics: 12th International Conference, WINE 2016 …, 2016
442016
Dominantly truthful multi-task peer prediction with a constant number of tasks
Y Kong
Proceedings of the fourteenth annual acm-siam symposium on discrete …, 2020
422020
Max-mig: an information theoretic approach for joint learning from crowds
P Cao, Y Xu, Y Kong, Y Wang
arXiv preprint arXiv:1905.13436, 2019
422019
L_dmi: An information-theoretic noise-robust loss function
Y Xu, P Cao, Y Kong, Y Wang
arXiv preprint arXiv:1909.03388, 2019
412019
Water from two rocks: Maximizing the mutual information
Y Kong, G Schoenebeck
Proceedings of the 2018 ACM Conference on Economics and Computation, 177-194, 2018
372018
Equilibrium selection in information elicitation without verification via information monotonicity
Y Kong, G Schoenebeck
arXiv preprint arXiv:1603.07751, 2016
352016
A framework for designing information elicitation mechanisms that reward truth-telling
Y Kong, G Schoenebeck
arXiv preprint arXiv:1605.01021, 15, 2016
222016
Tcgm: An information-theoretic framework for semi-supervised multi-modality learning
X Sun, Y Xu, P Cao, Y Kong, L Hu, S Zhang, Y Wang
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
212020
Information elicitation mechanisms for statistical estimation
Y Kong, G Schoenebeck, B Tao, FY Yu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (02), 2095-2102, 2020
202020
Eliciting expertise without verification
Y Kong, G Schoenebeck
Proceedings of the 2018 ACM Conference on Economics and Computation, 195-212, 2018
162018
f-similarity preservation loss for soft labels: A demonstration on cross-corpus speech emotion recognition
B Zhang, Y Kong, G Essl, EM Provost
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5725-5732, 2019
132019
Survey equivalence: A procedure for measuring classifier accuracy against human labels
P Resnick, Y Kong, G Schoenebeck, T Weninger
arXiv preprint arXiv:2106.01254, 2021
102021
Optimizing Bayesian information revelation strategy in prediction markets: the Alice Bob Alice case
Y Kong, G Schoenebeck
9th Innovations in Theoretical Computer Science Conference (ITCS 2018), 2018
102018
Learning to bid in repeated first-price auctions with budgets
Q Wang, Z Yang, X Deng, Y Kong
International Conference on Machine Learning, 36494-36513, 2023
62023
L_dmi: An information-theoretic noiserobust loss function. NeurIPS
Y Xu, P Cao, Y Kong, Y Wang
arXiv preprint arXiv:1909.03388, 2019
62019
Eliciting thinking hierarchy without a prior
Y Kong, Y Li, Y Zhang, Z Huang, J Wu
Advances in Neural Information Processing Systems 35, 13329-13341, 2022
52022
False consensus, information theory, and prediction markets
Y Kong, G Schoenebeck
arXiv preprint arXiv:2206.02993, 2022
42022
Truthful information elicitation from hybrid crowds
Q Han, S Ruan, Y Kong, A Liu, F Mohsin, L Xia
arXiv preprint arXiv:2107.10119, 2021
32021
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