Markus Weimer

Most-cited papers

Parallelized Stochastic Gradient Descent
Martin A. Zinkevich, Markus Weimer, Lihong Li, Alex Smola
2010Neural Information Processing Systems1,447 citations
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FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
2019Conference on Machine Learning and Systems352 citations
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The Yahoo! Music Dataset and KDD-Cup '11
Gideon Dror, Noam Koenigstein, Y. Koren, Markus Weimer
2012KDD Cup329 citations
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Machine learning on Big Data
Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer
2013IEEE International Conference on Data Engineering231 citations
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Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010)
Peter Brusilovsky, Iván Cantador, Y. Koren, T. Kuflik +1 more
2010ACM Conference on Recommender Systems204 citations Open access
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Improving maximum margin matrix factorization
Markus Weimer, Alexandros Karatzoglou, A. Smola
2008Machine-mediated learning152 citations Open access
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PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, M. Santambrogio +2 more
2018USENIX Symposium on Operating Systems Design and Implementation133 citations
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Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
2018International Conference on Learning Representations116 citations
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Automatically Assessing the Post Quality in Online Discussions on Software
Markus Weimer, Iryna Gurevych, M. Mühlhäuser
2007Annual Meeting of the Association for Computational Linguistics113 citations Open access
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Machine Learning at Microsoft with ML.NET
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr +30 more
2019Knowledge Discovery and Data Mining89 citations Open access
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Extending Relational Query Processing with ML Inference
K. Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas +9 more
2019Conference on Innovative Data Systems Research82 citations
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Vamsa: Automated Provenance Tracking in Data Science Scripts
M. Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan +4 more
2020Knowledge Discovery and Data Mining77 citations Open access
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Iterative MapReduce for Large Scale Machine Learning
Joshua Rosen, Neoklis Polyzotis, Vinayak R. Borkar, Yingyi Bu +4 more
2013arXiv.org77 citations
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A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu, K. Karanasos +3 more
2020USENIX Symposium on Operating Systems Design and Implementation71 citations
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Darmstadt Knowledge Processing Repository Based on UIMA
Iryna Gurevych, M. Mühlhäuser, C. Mueller, Juergen Steimle +2 more
200763 citations
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Predicting the perceived quality of web forum posts
Markus Weimer, Iryna Gurevych
200761 citations
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Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, D. Mahajan, C. Curino +2 more
2016IEEE Data Engineering Bulletin58 citations
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Data Science through the looking glass and what we found there
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi +7 more
2019arXiv.org53 citations
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Scaling Datalog for Machine Learning on Big Data
Yingyi Bu, Vinayak R. Borkar, M. Carey, Joshua Rosen +4 more
2012arXiv.org52 citations
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Collaborative Filtering on a Budget
Alexandros Karatzoglou, Alex Smola, Markus Weimer
2010International Conference on Artificial Intelligence and Statistics50 citations
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REEF: Retainable Evaluator Execution Framework
Markus Weimer, Ying-Da Chen, Byung-Gon Chun, Tyson Condie +13 more
2015SIGMOD Conference48 citations Open access
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Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, C. Curino, Avrilia Floratou +18 more
2019Conference on Innovative Data Systems Research44 citations
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Data Science Through the Looking Glass
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel +10 more
2022SIGMOD record39 citations
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Building Continuous Integration Services for Machine Learning
Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu +6 more
2020Knowledge Discovery and Data Mining36 citations
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Coded Elastic Computing
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar +2 more
2018International Symposium on Information Theory34 citations Open access
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Adaptive collaborative filtering
Markus Weimer, Alexandros Karatzoglou, A. Smola
2008ACM Conference on Recommender Systems34 citations
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Declarative Systems for Large-Scale Machine Learning
Vinayak R. Borkar, Yingyi Bu, M. Carey, Joshua Rosen +4 more
2012IEEE Data Engineering Bulletin33 citations
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Machine learning in ScalOps , a higher order cloud computing language
Markus Weimer, Tyson Condie, R. Ramakrishnan
201133 citations
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MLSys: The New Frontier of Machine Learning Systems
Alexander J. Ratner, Dan Alistarh, G. Alonso, D. Andersen +65 more
201925 citations
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SysML: The New Frontier of Machine Learning Systems
Alexander J. Ratner, Dan Alistarh, G. Alonso, D. Andersen +62 more
2019arXiv.org23 citations
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PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
2019arXiv.org23 citations
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Towards Resource-Elastic Machine Learning
D. Mahajan, Sundararajan Sellamanickam, Markus Weimer, Keerthi Selvaraj
201323 citations
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From the Edge to the Cloud: Model Serving in ML.NET
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer +1 more
2018IEEE Data Engineering Bulletin22 citations
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MLOS: An Infrastructure for Automated Software Performance Engineering
C. Curino, Neha Godwal, Brian Kroth, S. Kuryata +8 more
2020DEEM@SIGMOD21 citations Open access
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Automating System Configuration of Distributed Machine Learning
Woo-Yeon Lee, Markus Weimer, Brian Cho, Byung-Gon Chun +8 more
2019IEEE International Conference on Distributed Computing Systems16 citations
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A Convenient Framework for Efficient Parallel Multipass Algorithms
Markus Weimer, Sriram Rao, Martin A. Zinkevich
201016 citations
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WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni +4 more
2021Proceedings of the VLDB Endowment15 citations Open access
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Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Peter Brusilovsky, Iván Cantador, Y. Koren, T. Kuflik +1 more
2010ACM Conference on Recommender Systems15 citations
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PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!
H. S. Hossain, Marc T. Friedman, Hiren Patel, S. Qiao +10 more
2021Proceedings of the VLDB Endowment13 citations
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Dolphin : Runtime Optimization for Distributed Machine Learning
Byung-Gon Chun, Brian Cho, Beomyeol Jeon, Joo Seong Jeong +7 more
201611 citations
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Quantile Matrix Factorization for Collaborative Filtering
Alexandros Karatzoglou, Markus Weimer
2010International Conference on Electronic Commerce and Web Technologies11 citations
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Compiling Classical ML Pipelines into Tensor Computations for One-size-ﬁts-all Prediction Serving
S. Nakandala, Gyeong-In Yu, Markus Weimer, Matteo Interlandi
20197 citations
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Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, D. Mahajan, S. Keerthi, S. Vishwanathan +1 more
2017International Conference on Artificial Intelligence and Statistics7 citations
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The Need for Tabular Representation Learning: An Industry Perspective
A. Savelieva, Andreas Mueller, Avrilia Floratou, C. Curino +7 more
20226 citations
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Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts
Fotis Psallidas, Megan Leszczynski, M. Namaki, Avrilia Floratou +7 more
2023SIGMOD Conference Companion5 citations Open access
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Apache REEF
Byung-Gon Chun, Tyson Condie, Ying-Da Chen, Brian Cho +23 more
2017ACM Transactions on Computer Systems5 citations Open access
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Towards High-Performance Prediction Serving Systems
Yunseong Lee, Matteo Interlandi, Markus Weimer
20175 citations
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Improving Maximum Margin Matrix Margin Factorization
Markus Weimer, Alexandros Karatzoglou, Alex Smola
20085 citations
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Taming Model Serving Complexity, Performance and Cost: A Compilation to Tensor Computations Approach
Supun Nakandalam, Karla Saur, Gyeong-In Yu, K. Karanasos +3 more
20204 citations
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Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni +3 more
2019arXiv.org4 citations
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