Brendan Walsh Mark Hamilton Greg Newby Xi Wang 0016 Serena Ruan Sheng Zhao Lei He 0005 Shaofei Zhang Eric Dettinger William T. Freeman Markus Weimer Large-Scale Automatic Audiobook Creation. 2023 abs/2309.03926 CoRR https://doi.org/10.48550/arXiv.2309.03926 db/journals/corr/corr2309.html#abs-2309-03926
Fotis Psallidas Yiwen Zhu Bojan Karlas Jordan Henkel Matteo Interlandi Subru Krishnan Brian Kroth K. Venkatesh Emani Wentao Wu 0001 Ce Zhang 0001 Markus Weimer Avrilia Floratou Carlo Curino Konstantinos Karanasos Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. 30-37 2022 51 SIGMOD Rec. 2 https://doi.org/10.1145/3552490.3552496 db/journals/sigmod/sigmod51.html#PsallidasZKHIKK22
H. M. Sajjad Hossain Marc T. Friedman Hiren Patel Shi Qiao 0001 Soundar Srinivasan Markus Weimer Remmelt Ammerlaan Lucas Rosenblatt Gilbert Antonius Peter Orenberg Vijay Ramani Abhishek Roy 0008 Irene Shaffer Alekh Jindal PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 3362-3375 2021 14 Proc. VLDB Endow. 13 http://www.vldb.org/pvldb/vol14/p3362-hossain.pdf https://doi.org/10.14778/3484224.3484233 db/journals/pvldb/pvldb14.html#HossainFPQSWARA21
Gyeong-In Yu Saeed Amizadeh Sehoon Kim Artidoro Pagnoni Ce Zhang 0001 Byung-Gon Chun Markus Weimer Matteo Interlandi WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. 11-20 2021 15 Proc. VLDB Endow. 1 http://www.vldb.org/pvldb/vol15/p11-yu.pdf https://doi.org/10.14778/3485450.3485452 db/journals/pvldb/pvldb15.html#YuAKPZCWI21
Carlo Curino Neha Godwal Brian Kroth Sergiy Kuryata Greg Lapinski Siqi Liu Slava Oks Olga Poppe Adam Smiechowski Ed Thayer Markus Weimer Yiwen Zhu MLOS: An Infrastructure for Automated Software Performance Engineering. 2020 abs/2006.02155 CoRR https://arxiv.org/abs/2006.02155 db/journals/corr/corr2006.html#abs-2006-02155
Supun Nakandala Karla Saur Gyeong-In Yu Konstantinos Karanasos Carlo Curino Markus Weimer Matteo Interlandi A Tensor Compiler for Unified Machine Learning Prediction Serving. 2020 abs/2010.04804 CoRR https://arxiv.org/abs/2010.04804 db/journals/corr/corr2010.html#abs-2010-04804
Saeed Amizadeh Sergiy Matusevych Markus Weimer PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. 2019 abs/1903.01969 CoRR http://arxiv.org/abs/1903.01969 db/journals/corr/corr1903.html#abs-1903-01969
Alexander Ratner Dan Alistarh Gustavo Alonso David G. Andersen Peter Bailis Sarah Bird Nicholas Carlini Bryan Catanzaro Eric S. Chung Bill Dally Jeff Dean Inderjit S. Dhillon Alexandros G. Dimakis Pradeep Dubey Charles Elkan Grigori Fursin Gregory R. Ganger Lise Getoor Phillip B. Gibbons Garth A. Gibson Joseph E. Gonzalez Justin Gottschlich Song Han 0003 Kim M. Hazelwood Furong Huang Martin Jaggi Kevin G. Jamieson Michael I. Jordan Gauri Joshi Rania Khalaf Jason Knight Jakub Konecný Tim Kraska Arun Kumar 0001 Anastasios Kyrillidis Jing Li 0073 Samuel Madden 0001 H. Brendan McMahan Erik Meijer 0001 Ioannis Mitliagkas Rajat Monga Derek Gordon Murray Dimitris S. Papailiopoulos Gennady Pekhimenko Theodoros Rekatsinas Afshin Rostamizadeh Christopher Ré Christopher De Sa Hanie Sedghi Siddhartha Sen 0001 Virginia Smith Alex Smola Dawn Song Evan Randall Sparks Ion Stoica Vivienne Sze Madeleine Udell Joaquin Vanschoren Shivaram Venkataraman Rashmi Vinayak Markus Weimer Andrew Gordon Wilson Eric P. Xing Matei Zaharia Ce Zhang 0001 Ameet Talwalkar SysML: The New Frontier of Machine Learning Systems. 2019 abs/1904.03257 CoRR http://arxiv.org/abs/1904.03257 db/journals/corr/corr1904.html#abs-1904-03257
Zeeshan Ahmed Saeed Amizadeh Mikhail Bilenko Rogan Carr Wei-Sheng Chin Yael Dekel Xavier Dupré Vadim Eksarevskiy Eric Erhardt Costin Eseanu Senja Filipi Tom Finley Abhishek Goswami Monte Hoover Scott Inglis Matteo Interlandi Shon Katzenberger Najeeb Kazmi Gleb Krivosheev Pete Luferenko Ivan Matantsev Sergiy Matusevych Shahab Moradi Gani Nazirov Justin Ormont Gal Oshri Artidoro Pagnoni Jignesh Parmar Prabhat Roy Sarthak Shah Mohammad Zeeshan Siddiqui Markus Weimer Shauheen Zahirazami Yiwen Zhu Machine Learning at Microsoft with ML .NET. 2019 abs/1905.05715 CoRR http://arxiv.org/abs/1905.05715 db/journals/corr/corr1905.html#abs-1905-05715
Gyeong-In Yu Saeed Amizadeh Artidoro Pagnoni Byung-Gon Chun Markus Weimer Matteo Interlandi Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. 2019 abs/1906.03822 CoRR http://arxiv.org/abs/1906.03822 db/journals/corr/corr1906.html#abs-1906-03822
Ashvin Agrawal Rony Chatterjee Carlo Curino Avrilia Floratou Neha Godwal Matteo Interlandi Alekh Jindal Konstantinos Karanasos Subru Krishnan Brian Kroth Jyoti Leeka Kwanghyun Park 0001 Hiren Patel Olga Poppe Fotis Psallidas Raghu Ramakrishnan 0001 Abhishek Roy 0008 Karla Saur Rathijit Sen Markus Weimer Travis Wright Yiwen Zhu Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML. 2019 abs/1909.00084 CoRR http://arxiv.org/abs/1909.00084 db/journals/corr/corr1909.html#abs-1909-00084
Konstantinos Karanasos Matteo Interlandi Doris Xin Fotis Psallidas Rathijit Sen Kwanghyun Park 0001 Ivan Popivanov Supun Nakandala Subru Krishnan Markus Weimer Yuan Yu Raghu Ramakrishnan 0001 Carlo Curino Extending Relational Query Processing with ML Inference. 2019 abs/1911.00231 CoRR http://arxiv.org/abs/1911.00231 db/journals/corr/corr1911.html#abs-1911-00231
Fotis Psallidas Yiwen Zhu Bojan Karlas Matteo Interlandi Avrilia Floratou Konstantinos Karanasos Wentao Wu 0001 Ce Zhang 0001 Subru Krishnan Carlo Curino Markus Weimer Data Science through the looking glass and what we found there. 2019 abs/1912.09536 CoRR http://arxiv.org/abs/1912.09536 db/journals/corr/corr1912.html#abs-1912-09536
Yunseong Lee Alberto Scolari Byung-Gon Chun Markus Weimer Matteo Interlandi From the Edge to the Cloud: Model Serving in ML.NET. 46-53 2018 41 IEEE Data Eng. Bull. 4 http://sites.computer.org/debull/A18dec/p46.pdf db/journals/debu/debu41.html#LeeSCWI18
Yunseong Lee Alberto Scolari Byung-Gon Chun Marco Domenico Santambrogio Markus Weimer Matteo Interlandi PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. 2018 abs/1810.06115 CoRR http://arxiv.org/abs/1810.06115 db/journals/corr/corr1810.html#abs-1810-06115
Yaoqing Yang Matteo Interlandi Pulkit Grover Soummya Kar Saeed Amizadeh Markus Weimer Coded Elastic Computing. 2018 abs/1812.06411 CoRR http://arxiv.org/abs/1812.06411 db/journals/corr/corr1812.html#abs-1812-06411
Ignacio Cano 0001 Markus Weimer Dhruv Mahajan 0001 Carlo Curino Giovanni Matteo Fumarola Arvind Krishnamurthy Towards Geo-Distributed Machine Learning. 41-59 2017 40 IEEE Data Eng. Bull. 4 http://sites.computer.org/debull/A17dec/p41.pdf db/journals/debu/debu40.html#CanoWMCFK17
Byung-Gon Chun Tyson Condie Yingda Chen Brian Cho Andrew Chung Carlo Curino Chris Douglas Matteo Interlandi Beomyeol Jeon Joo Seong Jeong Gyewon Lee Yunseong Lee Tony Majestro Dahlia Malkhi Sergiy Matusevych Brandon Myers Mariia Mykhailova Shravan M. Narayanamurthy Joseph Noor Raghu Ramakrishnan 0001 Sriram Rao Russell Sears Beysim Sezgin Taegeon Um Julia Wang Markus Weimer Youngseok Yang Apache REEF: Retainable Evaluator Execution Framework. 5:1-5:31 2017 35 ACM Trans. Comput. Syst. 2 https://doi.org/10.1145/3132037 db/journals/tocs/tocs35.html#ChunCCCCCDIJJLL17
Michal Derezinski Dhruv Mahajan 0001 S. Sathiya Keerthi S. V. N. Vishwanathan Markus Weimer Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. 2017 abs/1704.06731 CoRR http://arxiv.org/abs/1704.06731 db/journals/corr/corr1704.html#DerezinskiMKVW17
Ignacio Cano 0001 Markus Weimer Dhruv Mahajan 0001 Carlo Curino Giovanni Matteo Fumarola Towards Geo-Distributed Machine Learning. 2016 abs/1603.09035 CoRR http://arxiv.org/abs/1603.09035 db/journals/corr/corr1603.html#CanoWMCF16
Byung-Gon Chun Tyson Condie Carlo Curino Raghu Ramakrishnan 0001 Russell Sears Markus Weimer REEF: Retainable Evaluator Execution Framework. 1370-1373 2013 6 Proc. VLDB Endow. 12 http://www.vldb.org/pvldb/vol6/p1370-sears.pdf https://doi.org/10.14778/2536274.2536318 db/journals/pvldb/pvldb6.html#ChunCCRSW13
Joshua Rosen Neoklis Polyzotis Vinayak R. Borkar Yingyi Bu Michael J. Carey 0001 Markus Weimer Tyson Condie Raghu Ramakrishnan 0001 Iterative MapReduce for Large Scale Machine Learning http://arxiv.org/abs/1303.3517 2013 CoRR abs/1303.3517 db/journals/corr/corr1303.html#abs-1303-3517
Vinayak R. Borkar Yingyi Bu Michael J. Carey 0001 Joshua Rosen Neoklis Polyzotis Tyson Condie Markus Weimer Raghu Ramakrishnan 0001 Declarative Systems for Large-Scale Machine Learning. 24-32 2012 35 IEEE Data Eng. Bull. 2 http://sites.computer.org/debull/A12june/declare.pdf db/journals/debu/debu35.html#BorkarBCRPCWR12
Yingyi Bu Vinayak R. Borkar Michael J. Carey 0001 Joshua Rosen Neoklis Polyzotis Tyson Condie Markus Weimer Raghu Ramakrishnan 0001 Scaling Datalog for Machine Learning on Big Data http://arxiv.org/abs/1203.0160 2012 CoRR abs/1203.0160 db/journals/corr/corr1203.html#abs-1203-0160
Markus Weimer Alexandros Karatzoglou Alexander J. Smola Improving maximum margin matrix factorization. 263-276 2008 72 Mach. Learn. 3 https://doi.org/10.1007/s10994-008-5073-7 db/journals/ml/ml72.html#WeimerKS08
