Compressed linear algebra for declarative large-scale machine learning
- Ahmed Elgohary
- Matthias Boehm
- et al.
- 2019
- CACM
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92 results for Peter J. Haas
Compressed linear algebra for large-scale machine learning
- Ahmed Elgohary
- Matthias Boehm
- et al.
- 2017
- VLDB Journal
Scaling machine learning via compressed linear algebra
- Ahmed Elgohary
- Matthias Boehm
- et al.
- 2017
- SIGMOD Record
Sampling for scalable visual analytics
- Bum Chul Kwon
- Janu Verma
- et al.
- 2017
- IEE CG&A
On transience and recurrence in irreducible finite-state stochastic systems
- Peter W. Glynn
- Peter J. Haas
- 2015
- ACM TOMACS
Shared-memory and shared-nothing stochastic gradient descent algorithms for matrix completion
- Faraz Makari
- Christina Teflioudi
- et al.
- 2014
- KAIS
Non-uniformity issues and workarounds in bounded-size sampling
- Rainer Gemulla
- Peter J. Haas
- et al.
- 2013
- VLDB Journal
Synopses for massive data: Samples, histograms, wavelets, sketches
- Graham Cormode
- Minos Garofalakis
- et al.
- 2011
- Foundations and Trends in Databases
Information technology for healthcare transformation
- Joseph P. Bigus
- M. Campbell
- et al.
- 2011
- IBM J. Res. Dev
Distinct-value synopses for multiset operations
- Kevin Beyer
- Rainer Gemulla
- et al.
- 2009
- Communications of the ACM