Papers
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz et al.
Multi-task Sparse Structure Learning with Gaussian Copula Models
André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing et al.
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
François Denis, Mattias Gybels, Amaury Habrard
A Gibbs Sampler for Learning DAGs
Robert J. B. Goudie, Sach Mukherjee
Consistent Distribution-Free K-Sample and Independence Tests for Univariate Random Variables
Ruth Heller, Yair Heller, Shachar Kaufman et al.
Non-linear Causal Inference using Gaussianity Measures
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz et al.
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Lucas Mentch, Giles Hooker
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
Hà Quang Minh, Loris Bazzani, Vittorio Murino
Learning Using Anti-Training with Sacrificial Data
Michael L. Valenzuela, Jerzy W. Rozenblit
A Closer Look at Adaptive Regret
Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov et al.
Gradients Weights improve Regression and Classification
Samory Kpotufe, Abdeslam Boularias, Thomas Schultz et al.
MEKA: A Multi-label/Multi-target Extension to WEKA
Jesse Read, Peter Reutemann, Bernhard Pfahringer et al.
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Philippe Preux et al.
Analysis of Classification-based Policy Iteration Algorithms
Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
Loss Minimization and Parameter Estimation with Heavy Tails
Daniel Hsu, Sivan Sabato
Extremal Mechanisms for Local Differential Privacy
Peter Kairouz, Sewoong Oh, Pramod Viswanath
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces
Xiang Zhang, Yichao Wu, Lan Wang et al.
LLORMA: Local Low-Rank Matrix Approximation
Joonseok Lee, Seungyeon Kim, Guy Lebanon et al.
Convex Calibration Dimension for Multiclass Loss Matrices
Harish G. Ramaswamy, Shivani Agarwal
Learning the Variance of the Reward-To-Go
Aviv Tamar, Dotan Di Castro, Shie Mannor
Noisy Sparse Subspace Clustering
Yu-Xiang Wang, Huan Xu
Complexity of Representation and Inference in Compositional Models with Part Sharing
Alan Yuille, Roozbeh Mottaghi
Herded Gibbs Sampling
Yutian Chen, Luke Bornn, Nando de Freitas et al.