Papers
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.
AdaDelay: Delay Adaptive Distributed Stochastic Optimization
Suvrit Sra, Adams Wei Yu, Mu Li et al.
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces
Amirali Abdullah, Ravi Kumar, Andrew McGregor et al.
Pareto Front Identification from Stochastic Bandit Feedback
Peter Auer, Chao-Kai Chiang, Ronald Ortner et al.
Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects
Mario Figueiredo, Robert Nowak
How to Learn a Graph from Smooth Signals
Vassilis Kalofolias
Pseudo-Marginal Slice Sampling
Iain Murray, Matthew Graham
Semi-Supervised Learning with Adaptive Spectral Transform
Hanxiao Liu, Yiming Yang
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games
Julien Pérolat, Bilal Piot, Bruno Scherrer et al.
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
Chun-Liang Li, Kirthevasan Kandasamy, Barnabas Poczos et al.
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
Model-based Co-clustering for High Dimensional Sparse Data
Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif
Scalable geometric density estimation
Ye Wang, Antonio Canale, David Dunson
Accelerating Online Convex Optimization via Adaptive Prediction
Mehryar Mohri, Scott Yang
Low-Rank Approximation of Weighted Tree Automata
Guillaume Rabusseau, Borja Balle, Shay Cohen
Variational Gaussian Copula Inference
Shaobo Han, Xuejun Liao, David Dunson et al.
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Xiaowei Hu, Prashanth L.A., András György et al.
Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider et al.
Stochastic Variational Inference for the HDP-HMM
Aonan Zhang, San Gultekin, John Paisley