Markus Weimer

According to our database, Markus Weimer authored at least 49 papers between 2007 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

Large-Scale Automatic Audiobook Creation.

Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer

CoRR, 2023

Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts.

Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

2022

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines.

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

SIGMOD Rec., 2022

2021

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele.

Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

Proc. VLDB Endow., 2021

PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!

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

Proc. VLDB Endow., 2021

FLAML: A Fast and Lightweight AutoML Library.

Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu

Proceedings of Machine Learning and Systems 2021, 2021

2020

MLOS: An Infrastructure for Automated Software Performance Engineering.

Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu

Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

A Tensor Compiler for Unified Machine Learning Prediction Serving.

Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, 2020

Vamsa: Automated Provenance Tracking in Data Science Scripts.

Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yinghui Wu, Yiwen Zhu, Markus Weimer

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Building Continuous Integration Services for Machine Learning.

Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Extending Relational Query Processing with ML Inference.

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

Proceedings of the 10th Conference on Innovative Data Systems Research, 2020

Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML.

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

Proceedings of the 10th Conference on Innovative Data Systems Research, 2020

2019

Data Science through the looking glass and what we found there.

Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer

CoRR, 2019

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

CoRR, 2019

Machine Learning at Microsoft with ML .NET.

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

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

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 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

CoRR, 2019

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers.

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

CoRR, 2019

Machine Learning at Microsoft with ML.NET.

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

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Coded Elastic Computing.

Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer

Proceedings of the IEEE International Symposium on Information Theory, 2019

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach.

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

Proceedings of the 7th International Conference on Learning Representations, 2019

Automating System Configuration of Distributed Machine Learning.

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

Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, 2019

2018

From the Edge to the Cloud: Model Serving in ML.NET.

Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

IEEE Data Eng. Bull., 2018

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems.

Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

RAW 2018 Invited Talks.

Jürgen Becker, Viktor K. Prasanna, Markus Weimer, Wayne Luk, Kaveh Aasaraai, Derek Chiou

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018

Batch-Expansion Training: An Efficient Optimization Framework.

Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Apache REEF: Retainable Evaluator Execution Framework.

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

ACM Trans. Comput. Syst., 2017

Towards Geo-Distributed Machine Learning.

Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind Krishnamurthy

IEEE Data Eng. Bull., 2017

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning.

Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer

CoRR, 2017

Towards Accelerating Generic Machine Learning Prediction Pipelines.

Alberto Scolari, Yunseong Lee, Markus Weimer, Matteo Interlandi

Proceedings of the 2017 IEEE International Conference on Computer Design, 2017

2016

Towards Geo-Distributed Machine Learning.

Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola

CoRR, 2016

2015

REEF: Retainable Evaluator Execution Framework.

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

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

2013

REEF: Retainable Evaluator Execution Framework.

Byung-Gon Chun, Tyson Condie, Carlo Curino, Raghu Ramakrishnan, Russell Sears, Markus Weimer

Proc. VLDB Endow., 2013

Iterative MapReduce for Large Scale Machine Learning

Joshua Rosen, Neoklis Polyzotis, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Markus Weimer, Tyson Condie, Raghu Ramakrishnan

CoRR, 2013

Machine learning on Big Data.

Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer

Proceedings of the 29th IEEE International Conference on Data Engineering, 2013

2012

The Yahoo! Music Dataset and KDD-Cup '11.

Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer

Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Declarative Systems for Large-Scale Machine Learning.

Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan

IEEE Data Eng. Bull., 2012

Scaling Datalog for Machine Learning on Big Data

Yingyi Bu, Vinayak R. Borkar, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan

CoRR, 2012

2011

Machine Teaching - a Machine Learning Approach to TEL.

Markus Weimer

Proceedings of the IATEL, 2011

2010

Machine teaching: a machine learning approach to technology enhanced learning.

Markus Weimer

PhD thesis, 2010

Collaborative Filtering on a Budget.

Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010).

Peter Brusilovsky, Iván Cantador, Yehuda Koren, Tsvi Kuflik, Markus Weimer

Proceedings of the 2010 ACM Conference on Recommender Systems, 2010

Parallelized Stochastic Gradient Descent.

Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Quantile Matrix Factorization for Collaborative Filtering.

Alexandros Karatzoglou, Markus Weimer

Proceedings of the E-Commerce and Web Technologies, 11th International Conference, 2010

Collaborative Preference Learning.

Alexandros Karatzoglou, Markus Weimer

Proceedings of the Preference Learning., 2010

2009

Maximum margin matrix factorization for code recommendation.

Markus Weimer, Alexandros Karatzoglou, Marcel Bruch

Proceedings of the 2009 ACM Conference on Recommender Systems, 2009

2008

Improving maximum margin matrix factorization.

Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola

Mach. Learn., 2008

Adaptive collaborative filtering.

Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola

Proceedings of the 2008 ACM Conference on Recommender Systems, 2008

2007

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .

Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Automatically Assessing the Post Quality in Online Discussions on Software.

Markus Weimer, Iryna Gurevych, Max Mühlhäuser

Proceedings of the ACL 2007, 2007