JMLR 2014
109 papers
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Oliveier Chapelle, Miroslav Dudík, John Langford Active Contextual Policy Search
Alexander Fabisch, Jan Hendrik Metzen Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders
Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Perez-Cruz Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf Classifier Cascades and Trees for Minimizing Feature Evaluation Cost
Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle Clustering Hidden Markov Models with Variational HEM
Emanuele Coviello, Antoni B. Chan, Gert R.G. Lanckriet Cover Tree Bayesian Reinforcement Learning
Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas Detecting Click Fraud in Online Advertising: A Data Mining Approach
Richard Oentaryo, Ee-Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Minh Nhut Nguyen, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov Effective String Processing and Matching for Author Disambiguation
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin Efficient Occlusive Components Analysis
Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke Efficient State-Space Inference of Periodic Latent Force Models
Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings Fast SVM Training Using Approximate Extreme Points
Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi Follow the Leader if You Can, Hedge if You Must
Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen Ground Metric Learning
Marco Cuturi, David Avis Learning Graphical Models with Hubs
Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten Natural Evolution Strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber Node-Based Learning of Multiple Gaussian Graphical Models
Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee On the Bayes-Optimality of F-Measure Maximizers
Willem Waegeman, Krzysztof Dembczyński, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön Prediction and Clustering in Signed Networks: A Local to Global Perspective
Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar Random Intersection Trees
Rajen Dinesh Shah, Nicolai Meinshausen Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers
Brett L Moore, Larry D Pyeatt, Vivekanand Kulkarni, Periklis Panousis, Kevin Padrez, Anthony G Doufas Robust Hierarchical Clustering
Maria-Florina Balcan, Yingyu Liang, Pramod Gupta Sparse Factor Analysis for Learning and Content Analytics
Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk Statistical Analysis of Metric Graph Reconstruction
Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman Structured Prediction via Output Space Search
Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli Tensor Decompositions for Learning Latent Variable Models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky Training Highly Multiclass Classifiers
Maya R. Gupta, Samy Bengio, Jason Weston