The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. Support is available through the mailing list. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it’s reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available), The code should be easily usable. Its only external dependence is on the boost library, which is often installed by default.
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References in zbMATH (referenced in 12 articles )
Showing results 1 to 12 of 12.
- Apishev, M. A.: Effective implementations of topic modeling algorithms (2021)
- Pratola, M. T.; Chipman, H. A.; George, E. I.; McCulloch, R. E.: Heteroscedastic BART via multiplicative regression trees (2020)
- Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (2019)
- El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
- Shah, Rajen D.; Meinshausen, Nicolai: On (b)-bit min-wise hashing for large-scale regression and classification with sparse data (2018)
- Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv
- Pinto, Jervis; Fern, Alan: Learning partial policies to speedup MDP tree search via reduction to i.i.d. learning (2017)
- Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Buntine, Wray; Hynes, Mike; De Sterck, Hans: Efficient parameter learning of Bayesian network classifiers (2017)
- Arndt, Cornelius; Brefeld, Ulf: Predicting the future performance of soccer players (2016)
- Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Cerquides, Jesús: (\textALR^n): accelerated higher-order logistic regression (2016)
- Kalyan Veeramachaneni; Ignacio Arnaldo; Owen Derby; Una-May O’Reilly: FlexGP (2015) not zbMATH
- Feng Niu, Benjamin Recht, Christopher Re, Stephen J. Wright: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent (2011) arXiv