Papers
4,122 papers found
FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw et al.
Gaussian Processes for Machine Learning (GPML) Toolbox
Carl Edward Rasmussen, Hannes Nickisch
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
Gideon S. Mann, Andrew McCallum
Generalized Power Method for Sparse Principal Component Analysis
Michel Journée, Yurii Nesterov, Peter Richtárik et al.
Graph Kernels
S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor et al.
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency
Dapo Omidiran, Martin J. Wainwright
Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu et al.
How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling et al.
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Miloš Radovanović
Image Denoising with Kernels Based on Natural Image Relations
Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls et al.
Importance Sampling for Continuous Time Bayesian Networks
Yu Fan, Jing Xu, Christian R. Shelton
Incremental Sigmoid Belief Networks for Grammar Learning
James Henderson, Ivan Titov
Inducing Tree-Substitution Grammars
Trevor Cohn, Phil Blunsom, Sharon Goldwater
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Jarkko Venna, Jaakko Peltonen, Kristian Nybo et al.
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
Nguyen Xuan Vinh, Julien Epps, James Bailey
Introduction to Causal Inference
Peter Spirtes
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang et al.
Kronecker Graphs: An Approach to Modeling Networks
Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg et al.
Large Scale Online Learning of Image Similarity Through Ranking
Gal Chechik, Varun Sharma, Uri Shalit et al.
Learnability, Stability and Uniform Convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro et al.
Learning From Crowds
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao et al.
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
Qiang Wu, Justin Guinney, Mauro Maggioni et al.
Learning Instance-Specific Predictive Models
Shyam Visweswaran, Gregory F. Cooper
Learning Non-Stationary Dynamic Bayesian Networks
Joshua W. Robinson, Alexander J. Hartemink