| Submodular maximization with nearly optimal approximation, adaptivity and query complexity M Fahrbach, V Mirrokni, M Zadimoghaddam ACM-SIAM Symposium on Discrete Algorithms (SODA), 255-273, 2019 | 84 | 2019 |
| Edge-weighted online bipartite matching M Fahrbach, Z Huang, R Tao, M Zadimoghaddam Journal of the ACM 69 (6), 45:1-45:35, 2022 | 79 | 2022 |
| Non-monotone submodular maximization with nearly optimal adaptivity and query complexity M Fahrbach, V Mirrokni, M Zadimoghaddam International Conference on Machine Learning (ICML), 1833-1842, 2019 | 59* | 2019 |
| Faster graph embeddings via coarsening M Fahrbach, G Goranci, R Peng, S Sachdeva, C Wang International Conference on Machine Learning (ICML), 2953-2963, 2020 | 25 | 2020 |
| Subquadratic Kronecker regression with applications to tensor decomposition M Fahrbach, G Fu, M Ghadiri Advances in Neural Information Processing Systems (NeurIPS), 28776-28789, 2022 | 24* | 2022 |
| Coefficients and roots of peak polynomials S Billey, M Fahrbach, A Talmage Experimental Mathematics 25 (2), 165-175, 2016 | 18 | 2016 |
| Graph sketching against adaptive adversaries applied to the minimum degree algorithm M Fahrbach, GL Miller, R Peng, S Sawlani, J Wang, SC Xu IEEE Symposium on Foundations of Computer Science (FOCS), 101-112, 2018 | 13 | 2018 |
| Sequential attention for feature selection T Yasuda, MH Bateni, L Chen, M Fahrbach, G Fu, V Mirrokni International Conference on Learning Representations (ICLR), 2023 | 11 | 2023 |
| Unified Embedding: Battle-tested feature representations for web-scale ML systems B Coleman, WC Kang, M Fahrbach, R Wang, L Hong, E Chi, D Cheng Advances in Neural Information Processing Systems (NeurIPS), 56234-56255, 2023 | 8 | 2023 |
| Approximately sampling elements with fixed rank in graded posets P Bhakta, B Cousins, M Fahrbach, D Randall ACM-SIAM Symposium on Discrete Algorithms (SODA), 1828-1838, 2017 | 8 | 2017 |
| Learning rate schedules in the presence of distribution shift M Fahrbach, A Javanmard, V Mirrokni, P Worah International Conference on Machine Learning (ICML), 9523-9546, 2023 | 7 | 2023 |
| Approximately optimal core shapes for tensor decompositions M Ghadiri, M Fahrbach, G Fu, V Mirrokni International Conference on Machine Learning (ICML), 11237-11254, 2023 | 7 | 2023 |
| Slow mixing of Glauber dynamics for the six-vertex model in the ordered phases M Fahrbach, D Randall International Conference on Randomization and Computation (RANDOM), 37:1-37:20, 2019 | 6 | 2019 |
| Analyzing Boltzmann samplers for Bose–Einstein condensates with Dirichlet generating functions M Bernstein, M Fahrbach, D Randall Meeting on Analytic Algorithmics and Combinatorics (ANALCO), 107-117, 2018 | 6 | 2018 |
| Nearly tight bounds for sandpile transience on the grid D Durfee, M Fahrbach, Y Gao, T Xiao ACM-SIAM Symposium on Discrete Algorithms (SODA), 605-624, 2018 | 5 | 2018 |
| A fast minimum degree algorithm and matching lower bound R Cummings, M Fahrbach, A Fatehpuria ACM-SIAM Symposium on Discrete Algorithms (SODA), 724-734, 2021 | 3 | 2021 |
| PriorBoost: An adaptive algorithm for learning from aggregate responses A Javanmard, M Fahrbach, V Mirrokni arXiv preprint arXiv:2402.04987, 2024 | 2 | 2024 |
| Practical performance guarantees for pipelined DNN inference A Archer, M Fahrbach, K Liu, P Prabhu Forty-first International Conference on Machine Learning, 2024 | 1* | 2024 |
| GIST: Greedy independent set thresholding for diverse data summarization M Fahrbach, S Ramalingam, M Zadimoghaddam, S Ahmadian, ... arXiv preprint arXiv:2405.18754, 2024 | | 2024 |
| Greedy PIG: Adaptive integrated gradients K Axiotis, S Abu-al-haija, L Chen, M Fahrbach, G Fu arXiv preprint arXiv:2311.06192, 2023 | | 2023 |
Vahab MirrokniGoogle Fellow, VP, Google ResearchVerified email at google.com