Markus Weimer

ORCID

Publication Activity (10 Years)

Years Active: 2007-2023
Publications (10 Years): 38

Top Venues

Darmstadt University of Technology (PHD)

Education History

Darmstadt University of Technology

PHD

Publications

Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer
Large-Scale Automatic Audiobook Creation. CoRR (2023)
Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer
Large-Scale Automatic Audiobook Creation. INTERSPEECH (2023)
Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu
Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts. SIGMOD Conference Companion (2023)
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, K. Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. SIGMOD Rec. 51 (2) (2022)
Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
FLAML: A Fast and Lightweight AutoML Library. MLSys (2021)
H. M. Sajjad Hossain, Marc T. Friedman, Hiren Patel, Shi Qiao, Soundar Srinivasan, Markus Weimer, Remmelt Ammerlaan, Lucas Rosenblatt, Gilbert Antonius, Peter Orenberg, Vijay Ramani, Abhishek Roy, Irene Shaffer, Alekh Jindal
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! Proc. VLDB Endow. 14 (13) (2021)
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. Proc. VLDB Endow. 15 (1) (2021)
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
A Tensor Compiler for Unified Machine Learning Prediction Serving. CoRR (2020)
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. DEEM@SIGMOD (2020)
Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML. CIDR (2020)
Konstantinos Karanasos, Matteo Interlandi, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Doris Xin, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
Extending Relational Query Processing with ML Inference. CIDR (2020)
Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yinghui Wu, Yiwen Zhu, Markus Weimer
Vamsa: Automated Provenance Tracking in Data Science Scripts. KDD (2020)
Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
Building Continuous Integration Services for Machine Learning. KDD (2020)
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
A Tensor Compiler for Unified Machine Learning Prediction Serving. OSDI (2020)
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. CoRR (2020)
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
Coded Elastic Computing. ISIT (2019)
Woo-Yeon Lee, Markus Weimer, Brian Cho, Byung-Gon Chun, Yunseong Lee, Joo Seong Jeong, Gyeong-In Yu, Jooyeon Kim, Hojin Park, Beomyeol Jeon, Won Wook Song, Gunhee Kim
Automating System Configuration of Distributed Machine Learning. ICDCS (2019)
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupré, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu
Machine Learning at Microsoft with ML.NET. KDD (2019)
Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML. CoRR (2019)
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. CoRR (2019)
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach. ICLR (Poster) (2019)
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. CoRR (2019)
Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
Extending Relational Query Processing with ML Inference. CoRR (2019)
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer
Data Science through the looking glass and what we found there. CoRR (2019)
Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric 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, 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, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, 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, Virginia Smith, Alex Smola, Dawn Song, Evan R. 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, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
From the Edge to the Cloud: Model Serving in ML.NET. IEEE Data Eng. Bull. 41 (4) (2018)
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. OSDI (2018)
Jürgen Becker, Viktor K. Prasanna, Markus Weimer, Wayne Luk, Kaveh Aasaraai, Derek Chiou
RAW 2018 Invited Talks. IPDPS Workshops (2018)
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS (2018)
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. CoRR (2018)
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
Coded Elastic Computing. CoRR (2018)
Alberto Scolari, Yunseong Lee, Markus Weimer, Matteo Interlandi
Towards Accelerating Generic Machine Learning Prediction Pipelines. ICCD (2017)
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR (2017)
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind Krishnamurthy
Towards Geo-Distributed Machine Learning. IEEE Data Eng. Bull. 40 (4) (2017)
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, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang
Apache REEF: Retainable Evaluator Execution Framework. ACM Trans. Comput. Syst. 35 (2) (2017)
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola
Towards Geo-Distributed Machine Learning. CoRR (2016)
Markus Weimer, Yingda Chen, Byung-Gon Chun, Tyson Condie, Carlo Curino, Chris Douglas, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Shravan M. Narayanamurthy, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Julia Wang
REEF: Retainable Evaluator Execution Framework. SIGMOD Conference (2015)
Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer
Machine learning on Big Data. ICDE (2013)
Joshua Rosen, Neoklis Polyzotis, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Markus Weimer, Tyson Condie, Raghu Ramakrishnan
Iterative MapReduce for Large Scale Machine Learning CoRR (2013)
Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer
Machine learning for big data. SIGMOD Conference (2013)
Byung-Gon Chun, Tyson Condie, Carlo Curino, Raghu Ramakrishnan, Russell Sears, Markus Weimer
REEF: Retainable Evaluator Execution Framework. Proc. VLDB Endow. 6 (12) (2013)
Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer
The Yahoo! Music Dataset and KDD-Cup '11. KDD Cup (2012)
Yingyi Bu, Vinayak R. Borkar, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan
Scaling Datalog for Machine Learning on Big Data CoRR (2012)
Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan
Declarative Systems for Large-Scale Machine Learning. IEEE Data Eng. Bull. 35 (2) (2012)
Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer
Collaborative Filtering on a Budget. AISTATS (2010)
Alexandros Karatzoglou, Markus Weimer
Quantile Matrix Factorization for Collaborative Filtering. EC-Web (2010)
Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li
Parallelized Stochastic Gradient Descent. NIPS (2010)
Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer
Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010). RecSys (2010)
Markus Weimer, Alexandros Karatzoglou, Marcel Bruch
Maximum margin matrix factorization for code recommendation. RecSys (2009)
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
Improving maximum margin matrix factorization. Mach. Learn. 72 (3) (2008)
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
Adaptive collaborative filtering. RecSys (2008)
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) (2008)
Markus Weimer, Iryna Gurevych, Max Mühlhäuser
Automatically Assessing the Post Quality in Online Discussions on Software. ACL (2007)
Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS (2007)

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Publication Activity (10 Years)

Top Topics

Top Venues

Markus Weimer