Markus Weimer

60 papers A* 7A 9B 1C 1Journal 25Unranked 12
YearRankTypeTitle / Venue / Authors
2023 — conf
Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts.
SIGMOD Conference Companion
Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu
2023 A conf
Large-Scale Automatic Audiobook Creation.
INTERSPEECH
Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer
2023 J jnl
Large-Scale Automatic Audiobook Creation.
CoRR
Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer
2022 J jnl
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines.
SIGMOD Rec.
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
2021 — conf
FLAML: A Fast and Lightweight AutoML Library.
MLSys
Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
2021 J jnl
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!
Proc. VLDB Endow.
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
2021 J jnl
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele.
Proc. VLDB Endow.
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
2020 A* conf
A Tensor Compiler for Unified Machine Learning Prediction Serving.
OSDI
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
2020 J jnl
A Tensor Compiler for Unified Machine Learning Prediction Serving.
CoRR
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
2020 A* conf
Building Continuous Integration Services for Machine Learning.
KDD
Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
2020 A conf
Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML.
CIDR
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
2020 A conf
Extending Relational Query Processing with ML Inference.
CIDR
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
2020 — conf
MLOS: An Infrastructure for Automated Software Performance Engineering.
DEEM@SIGMOD
Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu
2020 J jnl
MLOS: An Infrastructure for Automated Software Performance Engineering.
CoRR
Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu
2020 A* conf
Vamsa: Automated Provenance Tracking in Data Science Scripts.
KDD
Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yinghui Wu, Yiwen Zhu, Markus Weimer
2019 A conf
Automating System Configuration of Distributed Machine Learning.
ICDCS
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
2019 J jnl
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML.
CoRR
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
2019 B conf
Coded Elastic Computing.
ISIT
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
2019 J jnl
Data Science through the looking glass and what we found there.
CoRR
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer
2019 J jnl
Extending Relational Query Processing with ML Inference.
CoRR
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
2019 — conf
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach.
ICLR (Poster)
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
2019 J jnl
Machine Learning at Microsoft with ML .NET.
CoRR
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
2019 A* conf
Machine Learning at Microsoft with ML.NET.
KDD
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
2019 J jnl
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.
CoRR
Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
2019 J jnl
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers.
CoRR
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
2019 J jnl
SysML: The New Frontier of Machine Learning Systems.
CoRR
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 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
2018 A conf
Batch-Expansion Training: An Efficient Optimization Framework.
AISTATS
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
2018 J jnl
Coded Elastic Computing.
CoRR
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
2018 J jnl
From the Edge to the Cloud: Model Serving in ML.NET.
IEEE Data Eng. Bull.
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
2018 A* conf
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems.
OSDI
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi
2018 J jnl
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems.
CoRR
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi
2018 — conf
RAW 2018 Invited Talks.
IPDPS Workshops
Jürgen Becker, Viktor K. Prasanna, Markus Weimer, Wayne Luk, Kaveh Aasaraai, Derek Chiou
2017 J jnl
Apache REEF: Retainable Evaluator Execution Framework.
ACM Trans. Comput. Syst.
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
2017 J jnl
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning.
CoRR
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
2017 C conf
Towards Accelerating Generic Machine Learning Prediction Pipelines.
ICCD
Alberto Scolari, Yunseong Lee, Markus Weimer, Matteo Interlandi
2017 J jnl
Towards Geo-Distributed Machine Learning.
IEEE Data Eng. Bull.
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind Krishnamurthy
2016 J jnl
Towards Geo-Distributed Machine Learning.
CoRR
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola
2015 — conf
REEF: Retainable Evaluator Execution Framework.
SIGMOD Conference
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
2013 J jnl
Iterative MapReduce for Large Scale Machine Learning
CoRR
Joshua Rosen, Neoklis Polyzotis, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Markus Weimer, Tyson Condie, Raghu Ramakrishnan
2013 — conf
Machine learning for big data.
SIGMOD Conference
Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer
2013 A* conf
Machine learning on Big Data.
ICDE
Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer
2013 J jnl
REEF: Retainable Evaluator Execution Framework.
Proc. VLDB Endow.
Byung-Gon Chun, Tyson Condie, Carlo Curino, Raghu Ramakrishnan, Russell Sears, Markus Weimer
2012 J jnl
Declarative Systems for Large-Scale Machine Learning.
IEEE Data Eng. Bull.
Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan
2012 — ed.
Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011
KDD Cup
Gideon Dror, Yehuda Koren, Markus Weimer
2012 J jnl
Scaling Datalog for Machine Learning on Big Data
CoRR
Yingyi Bu, Vinayak R. Borkar, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan
2012 — conf
The Yahoo! Music Dataset and KDD-Cup '11.
KDD Cup
Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer
2011 — ch.
Machine Teaching - a Machine Learning Approach to TEL.
IATEL - Interdisciplinary Approaches to Technology-enhanced Learning
Markus Weimer
2010 A conf
Collaborative Filtering on a Budget.
AISTATS
Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer
2010 — ch.
Collaborative Preference Learning.
Preference Learning
Alexandros Karatzoglou, Markus Weimer
2010 — —
Machine teaching: a machine learning approach to technology enhanced learning.
Markus Weimer
2010 — conf
Parallelized Stochastic Gradient Descent.
NIPS
Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li
2010 — ed.
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec '10, Barcelona, Spain, September 26, 2010
HetRec@RecSys
Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer
2010 — conf
Quantile Matrix Factorization for Collaborative Filtering.
EC-Web
Alexandros Karatzoglou, Markus Weimer
2010 A conf
Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010).
RecSys
Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer
2009 A conf
Maximum margin matrix factorization for code recommendation.
RecSys
Markus Weimer, Alexandros Karatzoglou, Marcel Bruch
2008 A conf
Adaptive collaborative filtering.
RecSys
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
2008 — conf
Improving Maximum Margin Matrix Factorization.
ECML/PKDD (1)
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
2008 J jnl
Improving maximum margin matrix factorization.
Mach. Learn.
Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola
2007 A* conf
Automatically Assessing the Post Quality in Online Discussions on Software.
ACL
Markus Weimer, Iryna Gurevych, Max Mühlhäuser
2007 — conf
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
NIPS
Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola