| Applied machine learning at facebook: A datacenter infrastructure perspective K Hazelwood, S Bird, D Brooks, S Chintala, U Diril, D Dzhulgakov, ... 2018 IEEE International Symposium on High Performance Computer Architecture …, 2018 | 654 | 2018 |
| Fairlearn: A toolkit for assessing and improving fairness in AI S Bird, M Dudík, R Edgar, B Horn, R Lutz, V Milan, M Sameki, H Wallach, ... Microsoft, Tech. Rep. MSR-TR-2020-32, 2020 | 304 | 2020 |
| A hardware evaluation of cache partitioning to improve utilization and energy-efficiency while preserving responsiveness H Cook, M Moreto, S Bird, K Dao, DA Patterson, K Asanovic ACM SIGARCH Computer Architecture News 41 (3), 308-319, 2013 | 162 | 2013 |
| Tessellation: Space-time partitioning in a manycore client OS R Liu, K Klues, S Bird, S Hofmeyr, K Asanovic, J Kubiatowicz HotPar09, Berkeley, CA 3, 2009, 2009 | 156 | 2009 |
| A case for FAME: FPGA architecture model execution Z Tan, A Waterman, H Cook, S Bird, K Asanović, D Patterson Proceedings of the 37th annual international symposium on Computer …, 2010 | 113 | 2010 |
| Making contextual decisions with low technical debt A Agarwal, S Bird, M Cozowicz, L Hoang, J Langford, S Lee, J Li, ... arXiv preprint arXiv:1606.03966, 2016 | 96 | 2016 |
| Tessellation: Refactoring the OS around explicit resource containers with continuous adaptation JA Colmenares, G Eads, S Hofmeyr, S Bird, M Moretó, D Chou, ... Proceedings of the 50th Annual Design Automation Conference, 1-10, 2013 | 85 | 2013 |
| Performance characterization of SPEC CPU benchmarks on Intel’s Core microarchitecture based processor S Bird, A Phansalkar, LK John, A Mericas, R Indukuru SPEC Benchmark Workshop, 1-7, 2007 | 75 | 2007 |
| Fairness-aware machine learning: Practical challenges and lessons learned S Bird, K Kenthapadi, E Kiciman, M Mitchell Proceedings of the twelfth ACM international conference on web search and …, 2019 | 65 | 2019 |
| Exploring or exploiting? Social and ethical implications of autonomous experimentation in AI S Bird, S Barocas, K Crawford, F Diaz, H Wallach Workshop on Fairness, Accountability, and Transparency in Machine Learning, 2016 | 63 | 2016 |
| Resource management in the Tessellation manycore OS JA Colmenares, S Bird, H Cook, P Pearce, D Zhu, J Shalf, S Hofmeyr, ... HotPar10, Berkeley, CA, 2010 | 57 | 2010 |
| The representativeness of automated web crawls as a surrogate for human browsing D Zeber, S Bird, C Oliveira, W Rudametkin, I Segall, F Wollsén, M Lopatka Proceedings of The Web Conference 2020, 167-178, 2020 | 48 | 2020 |
| A multiworld testing decision service A Agarwal, S Bird, M Cozowicz, L Hoang, J Langford, S Lee, J Li, ... arXiv preprint arXiv:1606.03966 7, 2016 | 33 | 2016 |
| Sysml: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, P Bailis, S Bird, N Carlini, B Catanzaro, ... arXiv preprint arXiv:1904.03257, 2019 | 27 | 2019 |
| {PACORA}: Performance Aware Convex Optimization for Resource Allocation SL Bird, BJ Smith | 27 | 2011 |
| Replication: why we still can't browse in peace: on the uniqueness and reidentifiability of web browsing histories S Bird, I Segall, M Lopatka Sixteenth Symposium on Usable Privacy and Security (SOUPS 2020), 489-503, 2020 | 26 | 2020 |
| Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection S Bird, V Mishra, S Englehardt, R Willoughby, D Zeber, W Rudametkin, ... arXiv preprint arXiv:2003.04463, 2020 | 14 | 2020 |
| Mlsys: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... arXiv preprint arXiv:1904.03257, 2019 | 13 | 2019 |
| Exploring or exploiting S Bird, S Barocas, K Crawford, F Diaz, H Wallach Social and ethical implications of autonomous experimentation in AI, 2016 | 13 | 2016 |
| Optimizing Resource Allocations for Dynamic Interactive Applications SL Bird University of California, Berkeley, 2014 | 10 | 2014 |
