Papers
Variational Rejection Sampling
Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla et al.
Variational Inference based on Robust Divergences
Futoshi Futami, Issei Sato, Masashi Sugiyama
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh et al.
Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi et al.
One-shot Coresets: The Case of k-Clustering
Olivier Bachem, Mario Lucic, Silvio Lattanzi
Direct Learning to Rank And Rerank
Cynthia Rudin, Yining Wang
Stochastic Three-Composite Convex Minimization with a Linear Operator
Renbo Zhao, Volkan Cevher
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Zi Wang, Clement Gehring, Pushmeet Kohli et al.
Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi et al.
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Subsampling for Ridge Regression via Regularized Volume Sampling
Michal Derezinski, Manfred Warmuth
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Robert Gower, Nicolas Le Roux, Francis Bach
Variational inference for the multi-armed contextual bandit
Iñigo Urteaga, Chris Wiggins
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
Nonlinear Weighted Finite Automata
Tianyu Li, Guillaume Rabusseau, Doina Precup
Few-shot Generative Modelling with Generative Matching Networks
Sergey Bartunov, Dmitry Vetrov
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen, Shuyang Dai, Yunchen Pu et al.
Efficient and principled score estimation with Nyström kernel exponential families
Danica J. Sutherland, Heiko Strathmann, Michael Arbel et al.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Mathieu Sinn, Ambrish Rawat
Nonparametric Preference Completion
Julian Katz-Samuels, Clayton Scott
Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen et al.
Catalyst for Gradient-based Nonconvex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy et al.
An Optimization Approach to Learning Falling Rule Lists
Chaofan Chen, Cynthia Rudin
Transfer Learning on fMRI Datasets
Hejia Zhang, Po-Hsuan Chen, Peter Ramadge