| Crowdsourced Clustering: Querying Edges vs Triangles RK Vinayak, B Hassibi Advances in Neural Information Processing Systems, 2016 | 64 | 2016 |
| Maximum likelihood estimation for learning populations of parameters RK Vinayak, W Kong, G Valiant, S Kakade International Conference on Machine Learning, 6448-6457, 2019 | 61 | 2019 |
| Graph clustering with missing data: Convex algorithms and analysis R Korlakai Vinayak, S Oymak, B Hassibi Advances in Neural Information Processing Systems 27, 2014 | 50 | 2014 |
| Pal: Pluralistic alignment framework for learning from heterogeneous preferences D Chen, Y Chen, A Rege, RK Vinayak arXiv preprint arXiv:2406.08469, 2024 | 38 | 2024 |
| Sharp performance bounds for graph clustering via convex optimization RK Vinayak, S Oymak, B Hassibi 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 23 | 2014 |
| Promises and pitfalls of threshold-based auto-labeling H Vishwakarma, H Lin, F Sala, R Korlakai Vinayak Advances in Neural Information Processing Systems 36, 51955-51990, 2023 | 19 | 2023 |
| One for All: Simultaneous Metric and Preference Learning over Multiple Users. G Canal, B Mason, RK Vinayak, R Nowak NeurIPS, 2022 | 15 | 2022 |
| Similarity clustering in the presence of outliers: Exact recovery via convex program RK Vinayak, B Hassibi 2016 IEEE International Symposium on Information Theory (ISIT), 91-95, 2016 | 15 | 2016 |
| PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment D Chen, Y Chen, A Rege, Z Wang, RK Vinayak The Thirteenth International Conference on Learning Representations, 2025 | 14 | 2025 |
| Taming false positives in out-of-distribution detection with human feedback H Vishwakarma, H Lin, RK Vinayak arXiv preprint arXiv:2404.16954, 2024 | 13 | 2024 |
| Fisher-Pitman permutation tests based on nonparametric poisson mixtures with application to single cell genomics Z Miao, W Kong, RK Vinayak, W Sun, F Han Journal of the American Statistical Association 119 (545), 394-406, 2024 | 11 | 2024 |
| Metric learning from limited pairwise preference comparisons Z Wang, G So, RK Vinayak arXiv preprint arXiv:2403.19629, 2024 | 10 | 2024 |
| Estimating the number and effect sizes of non-null hypotheses J Brennan, RK Vinayak, K Jamieson International Conference on Machine Learning, 1123-1133, 2020 | 10 | 2020 |
| Crowdsourced clustering via active querying: Practical algorithm with theoretical guarantees Y Chen, RK Vinayak, B Hassibi Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 11 …, 2023 | 8 | 2023 |
| Pearls from pebbles: Improved confidence functions for auto-labeling H Vishwakarma, Y Chen, SJ Tay, SSS Namburi, F Sala, ... Advances in Neural Information Processing Systems 37, 15983-16015, 2024 | 7 | 2024 |
| Learning populations of preferences via pairwise comparison queries G Tatli, Y Chen, RK Vinayak International Conference on Artificial Intelligence and Statistics, 1720-1728, 2024 | 7 | 2024 |
| Unbiased face synthesis with diffusion models: Are we there yet SA Harrison Rosenberg, GV Ramesh, RK Vinayak, K Fawaz arXiv preprint arXiv:2309.07277 1 (2), 2023 | 7 | 2023 |
| Tensor-based crowdsourced clustering via triangle queries RK Vinayak, T Zrnic, B Hassibi 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 7 | 2017 |
| Pal: Pluralistic alignment framework for learning from heterogeneous preferences, 2024a D Chen, Y Chen, A Rege, RK Vinayak URL https://arxiv. org/abs/2406.08469, 0 | 6 | |
| Limitations of face image generation H Rosenberg, S Ahmed, G Ramesh, K Fawaz, RK Vinayak Proceedings of the AAAI Conference on Artificial Intelligence 38 (13), 14838 …, 2024 | 5 | 2024 |
Yi ChenDepartment of Electrical and Computer Engineering, University of Wisconsin-MadisonVerified email at wisc.edu
