Publications

This list is not updated frequently enough. You can find many of my more recent works on arxiv.org
PerfGuard: deploying ML-for-systems without performance regressions, almost!
Remmelt Ammerlaan, Gilbert Antonius, Marc Friedman, H M Sajjad Hossain, Alekh Jindal, Peter Orenberg, Hiren Patel, Shi Qiao, Vijay Ramani, Lucas Rosenblatt, Abhishek Roy, Irene Shaffer, Soundarajan Srinivasan, Markus Weimer
FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
Building Continuous Integration Services for Machine Learning
Bojan Karlaš, Matteo Interlandi, Cedric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
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
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
Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Gowdal, 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
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros 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 Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
From the Edge to the Cloud: Model Serving in ML.NET
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
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
Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind Krishnamurthy
Towards High-Performance Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Matteo Interlandi, Markus Weimer, Byung-Gon Chun
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 Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang
Salmon: Towards Production-Grade, Platform-Independent Distributed ML
Mikhail Bilenko, Tom Finley, Shon Katzenberger, Sebastian Kochman, Dhruv Mahajan, Shravan Narayanamurthy, Julia Wang, Shizhen Wang, Markus Weimer
Dolphin: Runtime Optimization for Distributed Machine Learning
Byung-Gon Chun, Brian Cho, Beomyeol Jeon, Joo Seong Jeong, Gunhee Kim, Joo Yeon Kim, Woo-Yeon Lee, Yun Seong Lee, Markus Weimer, Youngseok Yang, Gyeong-In Yu
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino and Giovanni Matteo Fumarola
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 Narayanamurthy, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Julia Wang
Elastic Distributed Bayesian Collaborative Filtering
Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay Narayanan
Towards Resource-Elastic Machine Learning
Shravan Narayanamurthy, Markus Weimer, Dhruv Mahajan, Tyson Condie, Sundararajan Sellamanickam, Keerthi Selvaraj
Distributed and Scalable PCA in the Cloud
Arun Kumar, Nikos Karampatziakis, Paul Mineiro, Markus Weimer and Vijay Narayanan
Declarative Systems for Large-Scale Machine Learning
Vinayak Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer and Raghu Ramakrishnan
WWW 2012 Tutorial: New Templates for Scalable Data Analysis
Alex Smola, Amr Ahmed, Markus Weimer
Machine learning in ScalOps, a higher order cloud computing language
Markus Weimer, Tyson Condie and Raghu Ramakrishnan
The Yahoo! Music Dataset and KDD-Cup’11
Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer
A Convenient Framework for Efficient Parallel Multipass Algorithms
Markus Weimer, Sriram Rao, Martin Zinkevich
Parallelized Stochastic Gradient Descent
Martin Zinkevich, Markus Weimer, Alex Smola, Lihong Li
Quantile Matrix Factorization for Collaborative Filtering
Alexandros Karatzoglou, Markus Weimer
Collaborative Filtering on a Budget
Alexandros Karatzoglou, Alex Smola, Markus Weimer
Machine Teaching -- A Machine Learning Approach to Technology Enhanced Learning
Markus Weimer
Maximum margin matrix factorization for code recommendation
Markus Weimer, Alexandros Karatzoglou and Marcel Bruch
Adaptive Collaborative Filtering
Markus Weimer, Alexandros Karatzoglou and Alexander J. Smola
Improving Maximum Margin Matrix Factorization
Markus Weimer, Alexandros Karatzoglou and Alexander J. Smola
CofiRank - Maximum Margin Matrix Factorization for Collaborative Ranking
Markus Weimer, Alexandros Karatzoglou, Quoc Viet Le, Alex Smola