Papers
Online Learning with Noisy Side Observations
Tomáš Kocák, Gergely Neu, Michal Valko
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Cote, Nando Freitas
Improper Deep Kernels
Uri Heinemann, Roi Livni, Elad Eban et al.
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Tom Goldstein, Gavin Taylor, Kawika Barabin et al.
Consistently Estimating Markov Chains with Noisy Aggregate Data
Garrett Bernstein, Daniel Sheldon
Topic-Based Embeddings for Learning from Large Knowledge Graphs
Changwei Hu, Piyush Rai, Lawrence Carin
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information
Changwei Hu, Piyush Rai, Lawrence Carin
Scalable and Sound Low-Rank Tensor Learning
Hao Cheng, Yaoliang Yu, Xinhua Zhang et al.
Determinantal Regularization for Ensemble Variable Selection
Veronika Rockova, Gemma Moran, Edward George
Bethe Learning of Graphical Models via MAP Decoding
Kui Tang, Nicholas Ruozzi, David Belanger et al.
Tight Variational Bounds via Random Projections and I-Projections
Lun-Kai Hsu, Tudor Achim, Stefano Ermon
Bayesian Nonparametric Kernel-Learning
Junier B. Oliva, Avinava Dubey, Andrew G. Wilson et al.
Early Stopping as Nonparametric Variational Inference
David Duvenaud, Dougal Maclaurin, Ryan Adams
Fitting Spectral Decay with the k-Support Norm
Andrew McDonald, Massimiliano Pontil, Dimitris Stamos
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Changyou Chen, David Carlson, Zhe Gan et al.
Inference for High-dimensional Exponential Family Graphical Models
Jialei Wang, Mladen Kolar
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko
Optimization as Estimation with Gaussian Processes in Bandit Settings
Zi Wang, Bolei Zhou, Stefanie Jegelka
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Hannes Nickisch et al.
Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh et al.
Unsupervised Feature Selection by Preserving Stochastic Neighbors
Xiaokai Wei, Philip S. Yu
Provable Bayesian Inference via Particle Mirror Descent
Bo Dai, Niao He, Hanjun Dai et al.
Globally Sparse Probabilistic PCA
Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche
Exponential Stochastic Cellular Automata for Massively Parallel Inference
Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan et al.