| Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet | 73 | 2019 |
| Spectral graph matching and regularized quadratic relaxations: Algorithm and theory Z Fan, C Mao, Y Wu, J Xu International conference on machine learning, 2985-2995, 2020 | 53 | 2020 |
| Scaling limits for the critical Fortuin–Kasteleyn model on a random planar map I: Cone times E Gwynne, C Mao, X Sun | 49* | 2019 |
| Minimax rates and efficient algorithms for noisy sorting C Mao, J Weed, P Rigollet Algorithmic Learning Theory, 821-847, 2018 | 49 | 2018 |
| Spectral graph matching and regularized quadratic relaxations II: Erdős-Rényi graphs and universality Z Fan, C Mao, Y Wu, J Xu Foundations of Computational Mathematics 23 (5), 1567-1617, 2023 | 48 | 2023 |
| Exact matching of random graphs with constant correlation C Mao, M Rudelson, K Tikhomirov Probability Theory and Related Fields 186 (1), 327-389, 2023 | 44 | 2023 |
| Worst-case versus average-case design for estimation from partial pairwise comparisons A Pananjady, C Mao, V Muthukumar, MJ Wainwright, TA Courtade The Annals of Statistics 48 (2), 1072-1097, 2020 | 44* | 2020 |
| Random graph matching at Otter’s threshold via counting chandeliers C Mao, Y Wu, J Xu, SH Yu Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1345-1356, 2023 | 37 | 2023 |
| Spectral graph matching and regularized quadratic relaxations I algorithm and Gaussian analysis Z Fan, C Mao, Y Wu, J Xu Foundations of Computational Mathematics 23 (5), 1511-1565, 2023 | 34* | 2023 |
| Random graph matching with improved noise robustness C Mao, M Rudelson, K Tikhomirov Conference on Learning Theory, 3296-3329, 2021 | 25 | 2021 |
| Towards optimal estimation of bivariate isotonic matrices with unknown permutations C Mao, A Pananjady, MJ Wainwright The Annals of Statistics 48 (6), 3183-3205, 2020 | 25 | 2020 |
| Optimal spectral recovery of a planted vector in a subspace C Mao, AS Wein arXiv preprint arXiv:2105.15081, 2021 | 23 | 2021 |
| Testing network correlation efficiently via counting trees C Mao, Y Wu, J Xu, SH Yu arXiv preprint arXiv:2110.11816, 2021 | 19 | 2021 |
| Breaking the Barrier: Faster Rates for Permutation-based Models in Polynomial Time C Mao, A Pananjady, MJ Wainwright Conference On Learning Theory, 2037-2042, 2018 | 15 | 2018 |
| Detection-recovery gap for planted dense cycles C Mao, AS Wein, S Zhang The Thirty Sixth Annual Conference on Learning Theory, 2440-2481, 2023 | 11 | 2023 |
| Estimation of Monge matrices JC Hütter, C Mao, P Rigollet, E Robeva | 11 | 2020 |
| Detection of dense subhypergraphs by low-degree polynomials A Dhawan, C Mao, AS Wein arXiv preprint arXiv:2304.08135, 2023 | 7 | 2023 |
| Learning mixtures of permutations: Groups of pairwise comparisons and combinatorial method of moments C Mao, Y Wu The Annals of Statistics 50 (4), 2231-2255, 2022 | 7 | 2022 |
| Information-Theoretic Thresholds for Planted Dense Cycles C Mao, AS Wein, S Zhang arXiv preprint arXiv:2402.00305, 2024 | 3 | 2024 |
| Optimal rates for estimation of two-dimensional totally positive distributions JC Hütter, C Mao, P Rigollet, E Robeva Electronic Journal of Statistics 14 (2), 2600-2652, 2020 | 3 | 2020 |
Yihong WuProfessor, Department of Statistics and Data ScienceVerified email at yale.edu
