Vowpal Wabbit

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.


References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Apishev, M. A.: Effective implementations of topic modeling algorithms (2021)
  2. Pratola, M. T.; Chipman, H. A.; George, E. I.; McCulloch, R. E.: Heteroscedastic BART via multiplicative regression trees (2020)
  3. Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (2019)
  4. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  5. Shah, Rajen D.; Meinshausen, Nicolai: On (b)-bit min-wise hashing for large-scale regression and classification with sparse data (2018)
  6. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv
  7. Pinto, Jervis; Fern, Alan: Learning partial policies to speedup MDP tree search via reduction to i.i.d. learning (2017)
  8. 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)
  9. Arndt, Cornelius; Brefeld, Ulf: Predicting the future performance of soccer players (2016)
  10. Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Cerquides, Jesús: (\textALR^n): accelerated higher-order logistic regression (2016)
  11. Kalyan Veeramachaneni; Ignacio Arnaldo; Owen Derby; Una-May O’Reilly: FlexGP (2015) not zbMATH
  12. Feng Niu, Benjamin Recht, Christopher Re, Stephen J. Wright: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent (2011) arXiv