Papers
Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman et al.
Latent Intention Dialogue Models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom et al.
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
Pengfei Wei, Ramon Sagarna, Yiping Ke et al.
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu
Beyond Filters: Compact Feature Map for Portable Deep Model
Yunhe Wang, Chang Xu, Chao Xu et al.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Jialei Wang, Lin Xiao
Variational Policy for Guiding Point Processes
Yichen Wang, Grady Williams, Evangelos Theodorou et al.
Sequence Modeling via Segmentations
Chong Wang, Yining Wang, Po-Sen Huang et al.
Tensor Decomposition via Simultaneous Power Iteration
Po-An Wang, Chi-Jen Lu
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Zi Wang, Chengtao Li, Stefanie Jegelka et al.
Robust Probabilistic Modeling with Bayesian Data Reweighting
Yixin Wang, Alp Kucukelbir, David M. Blei
Efficient Distributed Learning with Sparsity
Jialei Wang, Mladen Kolar, Nathan Srebro et al.
Max-value Entropy Search for Efficient Bayesian Optimization
Zi Wang, Stefanie Jegelka
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Lingxiao Wang, Quanquan Gu
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
Shusen Wang, Alex Gittens, Michael W. Mahoney
Capacity Releasing Diffusion for Speed and Locality
Di Wang, Kimon Fountoulakis, Monika Henzinger et al.
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudı́k
Fast Bayesian Intensity Estimation for the Permanental Process
Christian J. Walder, Adrian N. Bishop
On orthogonality and learning recurrent networks with long term dependencies
Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury et al.
Learning to Generate Long-term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou et al.
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Carlos Villacampa-Calvo, Daniel Hernández-Lobato
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul et al.
Model-Independent Online Learning for Influence Maximization
Sharan Vaswani, Branislav Kveton, Zheng Wen et al.
Automatic Discovery of the Statistical Types of Variables in a Dataset
Isabel Valera, Zoubin Ghahramani