A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Making large-scale support vector machine learning practical
Advances in kernel methods
Linear hinge loss and average margin
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Predicting Structured Data (Neural Information Processing)
Predicting Structured Data (Neural Information Processing)
Decoding by linear programming
IEEE Transactions on Information Theory
Training SVM with indefinite kernels
Proceedings of the 25th international conference on Machine learning
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
Estimating labels from label proportions
Proceedings of the 25th international conference on Machine learning
A quasi-Newton approach to non-smooth convex optimization
Proceedings of the 25th international conference on Machine learning
A sequential dual method for large scale multi-class linear svms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
A Fast Method for Training Linear SVM in the Primal
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Proximal regularization for online and batch learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large margin training for hidden Markov models with partially observed states
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Primal sparse Max-margin Markov networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse kernel SVMs via cutting-plane training
Machine Learning
Cutting-plane training of structural SVMs
Machine Learning
Learning the optimal neighborhood kernel for classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
The Journal of Machine Learning Research
Estimating Labels from Label Proportions
The Journal of Machine Learning Research
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Efficient structured support vector regression
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models
International Journal of Computer Vision
Training linear ranking SVMs in linearithmic time using red-black trees
Pattern Recognition Letters
A fast quasi-Newton method for semi-supervised SVM
Pattern Recognition
Optimization of robust loss functions for weakly-labeled image taxonomies: an imagenet case study
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Discriminative Models for Multi-Class Object Layout
International Journal of Computer Vision
Latent pyramidal regions for recognizing scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Image labeling on a network: using social-network metadata for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
The role of spatial context in activity recognition
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Estimating building simulation parameters via Bayesian structure learning
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
Proceedings of the 7th ACM international conference on Web search and data mining
| Hi-index | 0.00 |
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as l1 and l2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox.