Papers
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Adam S. Charles, Dong Yin, Christopher J. Rozell
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
Jason D. Lee, Qihang Lin, Tengyu Ma et al.
Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alexandre Allauzen et al.
Efficient Sampling from Time-Varying Log-Concave Distributions
Hariharan Narayanan, Alexer Rakhlin
Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
Jacques Wainer, Gavin Cawley
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich et al.
Faithfulness of Probability Distributions and Graphs
Kayvan Sadeghi
Fisher Consistency for Prior Probability Shift
Dirk Tasche
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, András György et al.
Fundamental Conditions for Low-CP-Rank Tensor Completion
Morteza Ashraphijuo, Xiaodong Wang
Gap Safe Screening Rules for Sparsity Enforcing Penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort et al.
Generalized Conditional Gradient for Sparse Estimation
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes
François Caron, Willie Neiswanger, Frank Wood et al.
Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
Jérémie Bigot, Charles Deledalle, Delphine Féral
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
GPflow: A Gaussian Process Library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson et al.
Group Sparse Optimization via lp,q Regularization
Yaohua Hu, Chong Li, Kaiwen Meng et al.
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach
Hierarchical Clustering via Spreading Metrics
Aurko Roy, Sebastian Pokutta
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, Vikas Sindhwani
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Stephen H. Bach, Matthias Broecheler, Bert Huang et al.
Identifying a Minimal Class of Models for High--dimensional Data
Daniel Nevo, Ya'acov Ritov
Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas
Improving Variational Methods via Pairwise Linear Response Identities
Jack Raymond, Federico Ricci-Tersenghi