Papers
GLASSES: Relieving The Myopia Of Bayesian Optimisation
Javier Gonzalez, Michael Osborne, Neil Lawrence
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm
Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
Sebastian Tschiatschek, Josip Djolonga, Andreas Krause
A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models
Rishit Sheth, Roni Khardon
Distributed Multi-Task Learning
Jialei Wang, Mladen Kolar, Nathan Srerbo
A Deep Generative Deconvolutional Image Model
Yunchen Pu, Win Yuan, Andrew Stevens et al.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu et al.
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Sungjin Ahn, Max Welling
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System
Yi Zhou, Yaoliang Yu, Wei Dai et al.
Variational Tempering
Stephan Mandt, James McInerney, Farhan Abrol et al.
Sequential Inference for Deep Gaussian Process
Yali Wang, Marcus Brubaker, Brahim Chaib-Draa et al.
Approximate Inference Using DC Programming For Collective Graphical Models
Thien Nguyen, Akshat Kumar, Hoong Chuin Lau et al.
Probabilistic Approximate Least-Squares
Simon Bartels, Philipp Hennig
Fast and Scalable Structural SVM with Slack Rescaling
Heejin Choi, Ofer Meshi, Nathan Srebro
Learning Relationships between Data Obtained Independently
Alexandra Carpentier, Teresa Schlueter
Nonparametric Budgeted Stochastic Gradient Descent
Trung Le, Vu Nguyen, Tu Dinh Nguyen et al.
Batch Bayesian Optimization via Local Penalization
Javier Gonzalez, Zhenwen Dai, Philipp Hennig et al.
New Resistance Distances with Global Information on Large Graphs
Canh Hao Nguyen, Hiroshi Mamitsuka
Fast Dictionary Learning with a Smoothed Wasserstein Loss
Antoine Rolet, Marco Cuturi, Gabriel Peyré
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
Fanhua Shang, Yuanyuan Liu, James Cheng
NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning
Eli Meirom, Pavel Kisilev
Low-Rank and Sparse Structure Pursuit via Alternating Minimization
Quanquan Gu, Zhaoran Wang Wang, Han Liu
Enumerating Equivalence Classes of Bayesian Networks using EC Graphs
Eunice Yuh-Jie Chen, Arthur Choi Choi, Adnan Darwiche
Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings
Zhitang Chen, Pascal Poupart, Yanhui Geng