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2020 – today
- 2023
[c30]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: 3675-3676- [c29]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: 123-126 - [i16]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 abs/2309.03926 (2023) - 2022
- [j9]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): 30-37 (2022) - 2021
- [j8]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): 3362-3375 (2021) - [j7]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): 11-20 (2021) - [c28]Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu:
FLAML: A Fast and Lightweight AutoML Library. MLSys 2021 - 2020
- [c27]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 - [c26]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 - [c25]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: 1542-1551 - [c24]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: 2407-2415 - [c23]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: 899-917 - [c22]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: 3:1-3:5 - [i15]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 abs/2006.02155 (2020) - [i14]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 abs/2010.04804 (2020)
2010 – 2019
- 2019
- [c21]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: 2057-2067 - [c20]Saeed Amizadeh, Sergiy Matusevych, Markus Weimer:
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach. ICLR (Poster) 2019 - [c19]Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer:
Coded Elastic Computing. ISIT 2019: 2654-2658 - [c18]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: 2448-2458 - [i13]Saeed Amizadeh, Sergiy Matusevych, Markus Weimer:
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. CoRR abs/1903.01969 (2019) - [i12]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, 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 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, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i11]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 abs/1905.05715 (2019) - [i10]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 abs/1906.03822 (2019) - [i9]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 abs/1909.00084 (2019) - [i8]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 abs/1911.00231 (2019) - [i7]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 abs/1912.09536 (2019) - 2018
- [j6]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): 46-53 (2018) - [c17]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS 2018: 736-744 - [c16]Jürgen Becker, Viktor K. Prasanna, Markus Weimer, Wayne Luk, Kaveh Aasaraai, Derek Chiou:
RAW 2018 Invited Talks. IPDPS Workshops 2018: 81-82 - [c15]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: 611-626 - [i6]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 abs/1810.06115 (2018) - [i5]Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer:
Coded Elastic Computing. CoRR abs/1812.06411 (2018) - 2017
- [j5]Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind Krishnamurthy:
Towards Geo-Distributed Machine Learning. IEEE Data Eng. Bull. 40(4): 41-59 (2017) - [j4]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): 5:1-5:31 (2017) - [c14]Alberto Scolari, Yunseong Lee, Markus Weimer, Matteo Interlandi:
Towards Accelerating Generic Machine Learning Prediction Pipelines. ICCD 2017: 431-434 - [i4]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR abs/1704.06731 (2017) - 2016
- [i3]Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola:
Towards Geo-Distributed Machine Learning. CoRR abs/1603.09035 (2016) - 2015
- [c13]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: 1343-1355 - 2013
- [j3]Byung-Gon Chun, Tyson Condie, Carlo Curino, Raghu Ramakrishnan, Russell Sears, Markus Weimer:
REEF: Retainable Evaluator Execution Framework. Proc. VLDB Endow. 6(12): 1370-1373 (2013) - [c12]Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer:
Machine learning on Big Data. ICDE 2013: 1242-1244 - [c11]Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer:
Machine learning for big data. SIGMOD Conference 2013: 939-942 - [i2]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 abs/1303.3517 (2013) - 2012
- [j2]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): 24-32 (2012) - [c10]Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer:
The Yahoo! Music Dataset and KDD-Cup '11. KDD Cup 2012: 8-18 - [e2]Gideon Dror, Yehuda Koren, Markus Weimer:
Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011. JMLR Proceedings 18, JMLR.org 2012 [contents] - [i1]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 abs/1203.0160 (2012) - 2011
- [p2]Markus Weimer:
Machine Teaching - a Machine Learning Approach to TEL. IATEL - Interdisciplinary Approaches to Technology-enhanced Learning 2011: 253-260 - 2010
- [b1]Markus Weimer:
Machine teaching: a machine learning approach to technology enhanced learning. Darmstadt University of Technology, 2010, pp. 1-148 - [c9]Alexandros Karatzoglou, Markus Weimer:
Quantile Matrix Factorization for Collaborative Filtering. EC-Web 2010: 253-264 - [c8]Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li:
Parallelized Stochastic Gradient Descent. NIPS 2010: 2595-2603 - [c7]Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer:
Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010). RecSys 2010: 375-376 - [c6]Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer:
Collaborative Filtering on a Budget. AISTATS 2010: 389-396 - [p1]Alexandros Karatzoglou, Markus Weimer:
Collaborative Preference Learning. Preference Learning 2010: 409-427 - [e1]Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer:
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec '10, Barcelona, Spain, September 26, 2010. ACM 2010, ISBN 978-1-4503-0407-8 [contents]
2000 – 2009
- 2009
- [c5]Markus Weimer, Alexandros Karatzoglou, Marcel Bruch:
Maximum margin matrix factorization for code recommendation. RecSys 2009: 309-312 - 2008
- [j1]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Improving maximum margin matrix factorization. Mach. Learn. 72(3): 263-276 (2008) - [c4]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) 2008: 14 - [c3]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Adaptive collaborative filtering. RecSys 2008: 275-282 - 2007
- [c2]Markus Weimer, Iryna Gurevych, Max Mühlhäuser:
Automatically Assessing the Post Quality in Online Discussions on Software. ACL 2007 - [c1]Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola:
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007: 1593-1600
Coauthor Index
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