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Parallelized Stochastic Gradient Descent
This paper presents the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence and introduces a novel proof technique — contractive mappings to quantify the speed of convergence of parameter distributions to their asymptotic limits.
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking
A method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating is presented and gives very good ranking scores and scales well on collaborative filtering tasks.
The Yahoo! Music Dataset and KDD-Cup '11
The organizers' account of the KDD-Cup 2011, which challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings, is provided, including a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.
Improving maximum margin matrix factorization
- Markus Weimer, Alexandros Karatzoglou, Alex Smola
- Computer ScienceMachine-mediated learning
- 15 September 2008
A number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph are discussed, showing equivalence between graph kernels and the recent MMMf extensions by Mnih and Salakhutdinov.
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
- S. Amizadeh, Sergiy Matusevych, Markus Weimer
- Computer ScienceInternational Conference on Learning…
- 27 September 2018
A neural framework that can learn to solve the Circuit Satisfiability problem by building upon a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem.
Predicting the perceived quality of web forum posts
This work proposes a system to assess the quality of user generated discourse automatically by applying SVM classification based on features such as Surface, Lexical, Syntactic, Forum specific and Similarity features and achieves an accuracy significantly higher than the baseline.
Automatically Assessing the Post Quality in Online Discussions on Software
- Markus Weimer, Iryna Gurevych, M. Mühlhäuser
- Computer ScienceAnnual Meeting of the Association for…
- 25 June 2007
An algorithm to assess the quality of forum posts automatically and test it on data provided by Nabble.com achieves an accuracy of 89% on the task of automatically assessing post quality in the software domain using forum specific features.
Scaling Datalog for Machine Learning on Big Data
This paper argues for the use of recursive queries to program a variety of machine learning systems using database query optimization techniques to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
- Yunseong Lee, Alberto Scolari, Byung-Gon Chun, M. Santambrogio, Markus Weimer, Matteo Interlandi
- Computer ScienceOSDI
- 8 October 2018
PRETZEL is a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations and is on average able to reduce 99th percentile latency while reducing memory footprint, and increasing throughput.
Coded Elastic Computing
- Yaoqing Yang, Matteo Interlandi, P. Grover, S. Kar, S. Amizadeh, Markus Weimer
- Computer ScienceInternational Symposium on Information Theory
- 16 December 2018
A new technique called coded elastic computing enabling distributed computations over elastic resources, which allows machines to leave the computation without sacrificing the algorithm-level performance, and flexibly reduce the workload at existing machines when new ones join the computation.