Markus Weimer

According to our database1, Markus Weimer authored at least 43 papers between 2007 and 2020.

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Bibliography

2020
A Tensor Compiler for Unified Machine Learning Prediction Serving.
CoRR, 2020

MLOS: An Infrastructure for Automated Software Performance Engineering.
Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

Vamsa: Automated Provenance Tracking in Data Science Scripts.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Building Continuous Integration Services for Machine Learning.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020



2019
Data Science through the looking glass and what we found there.
CoRR, 2019

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.
CoRR, 2019

Machine Learning at Microsoft with ML .NET.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers.
CoRR, 2019


Coded Elastic Computing.
Proceedings of the IEEE International Symposium on Information Theory, 2019

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach.
Proceedings of the 7th International Conference on Learning Representations, 2019

Automating System Configuration of Distributed Machine Learning.
Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, 2019

2018
From the Edge to the Cloud: Model Serving in ML.NET.
IEEE Data Eng. Bull., 2018

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems.
Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

RAW 2018 Invited Talks.
Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018

Batch-Expansion Training: An Efficient Optimization Framework.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Apache REEF: Retainable Evaluator Execution Framework.
ACM Trans. Comput. Syst., 2017

Towards Geo-Distributed Machine Learning.
IEEE Data Eng. Bull., 2017

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning.
CoRR, 2017

Towards Accelerating Generic Machine Learning Prediction Pipelines.
Proceedings of the 2017 IEEE International Conference on Computer Design, 2017

2016
Towards Geo-Distributed Machine Learning.
CoRR, 2016

2015
REEF: Retainable Evaluator Execution Framework.
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

2013
REEF: Retainable Evaluator Execution Framework.
Proc. VLDB Endow., 2013

Iterative MapReduce for Large Scale Machine Learning
CoRR, 2013

Machine learning on Big Data.
Proceedings of the 29th IEEE International Conference on Data Engineering, 2013

2012
The Yahoo! Music Dataset and KDD-Cup '11.
Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Declarative Systems for Large-Scale Machine Learning.
IEEE Data Eng. Bull., 2012

Scaling Datalog for Machine Learning on Big Data
CoRR, 2012

2011
Machine Teaching - a Machine Learning Approach to TEL.
Proceedings of the IATEL, 2011

2010
Machine teaching: a machine learning approach to technology enhanced learning.
PhD thesis, 2010

Collaborative Filtering on a Budget.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010).
Proceedings of the 2010 ACM Conference on Recommender Systems, 2010

Parallelized Stochastic Gradient Descent.
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.
Proceedings of the E-Commerce and Web Technologies, 11th International Conference, 2010

Collaborative Preference Learning.
Proceedings of the Preference Learning., 2010

2009
Maximum margin matrix factorization for code recommendation.
Proceedings of the 2009 ACM Conference on Recommender Systems, 2009

2008
Improving maximum margin matrix factorization.
Mach. Learn., 2008

Adaptive collaborative filtering.
Proceedings of the 2008 ACM Conference on Recommender Systems, 2008

2007
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Automatically Assessing the Post Quality in Online Discussions on Software.
Proceedings of the ACL 2007, 2007


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