| 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 | 210 | 2019 |
| 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 | 69 | 2019 |
| 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 | 45 | 2020 |
| 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 | 44 | 2019 |
| 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 | 44 | 2016 |
| L_dmi: An information-theoretic noise-robust loss function Y Xu, P Cao, Y Kong, Y Wang arXiv preprint arXiv:1909.03388, 2019 | 42 | 2019 |
| 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 | 38 | 2018 |
| Equilibrium selection in information elicitation without verification via information monotonicity Y Kong, G Schoenebeck arXiv preprint arXiv:1603.07751, 2016 | 36 | 2016 |
| 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 | 22 | 2020 |
| 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 | 22 | 2020 |
| A framework for designing information elicitation mechanisms that reward truth-telling Y Kong, G Schoenebeck arXiv preprint arXiv:1605.01021, 15, 2016 | 21 | 2016 |
| Eliciting expertise without verification Y Kong, G Schoenebeck Proceedings of the 2018 ACM Conference on Economics and Computation, 195-212, 2018 | 18 | 2018 |
| 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 | 13 | 2019 |
| 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 | 11 | 2021 |
| 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 | 10 | 2018 |
| 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 | 9 | 2023 |
| Algorithmic robust forecast aggregation Y Guo, JD Hartline, Z Huang, Y Kong, A Shah, FY Yu arXiv preprint arXiv:2401.17743, 2024 | 6 | 2024 |
| 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 | 6 | 2022 |
| L_dmi: An information-theoretic noiserobust loss function. NeurIPS Y Xu, P Cao, Y Kong, Y Wang arXiv preprint arXiv:1909.03388, 2019 | 6 | 2019 |
| False consensus, information theory, and prediction markets Y Kong, G Schoenebeck arXiv preprint arXiv:2206.02993, 2022 | 5 | 2022 |
