Matthew Fahrbach
Matthew is a Staff Research Scientist at Google in the Algorithms and Optimization group.
He received his PhD in computer science from the Georgia Institute of Technology, where he was advised by Dana Randall. Prior to that, he studied computer science and mathematics at the University of Kentucky. He is the recipient of a FOCS 2020 Best Paper Award, NSF Graduate Research Fellowship, and Barry Goldwater Scholarship. His research interests broadly include algorithms, discrete mathematics, machine learning, and optimization.
Authored Publications
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Practical Performance Guarantees for Pipelined DNN Inference
Kuikui Liu
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 1655-1671
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 21410-21429
Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri
Proceedings of the 40th International Conference on Machine Learning (2023), pp. 11237-11254
Learning Rate Schedules in the Presence of Distribution Shift
Adel Javanmard
Proceedings of the 40th International Conference on Machine Learning (2023), pp. 9523-9546
Sequential Attention for Feature Selection
Taisuke Yasuda
Lin Chen
Proceedings of the 11th International Conference on Learning Representations (2023)
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Ben Coleman
Ruoxi Wang
Lichan Hong
Advances in Neural Information Processing Systems (2023), pp. 56234-56255
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Mehrdad Ghadiri
Proceedings of the 36th Annual Conference on Neural Information Processing Systems (2022), pp. 28776-28789
Edge-Weighted Online Bipartite Matching
Runzhou Tao
Zhiyi Huang
Journal of the ACM, 69 (2022), 45:1-45:35
A Fast Minimum Degree Algorithm and Matching Lower Bound
Robert Cummings
Animesh Fatehpuria
Proceedings of the 32nd Annual ACM-SIAM Symposium on Discrete Algorithms (2021), pp. 724-734
Faster Graph Embeddings via Coarsening
Gramoz Goranci
Richard Peng
Sushant Sachdeva
Chi Wang
Proceedings of the 37th International Conference on Machine Learning (2020), pp. 2953-2963
