Papers
Semi-Supervised Learning with Adaptive Spectral Transform
Hanxiao Liu, Yiming Yang
Sequential Inference for Deep Gaussian Process
Yali Wang, Marcus Brubaker, Brahim Chaib-Draa et al.
Simple and Scalable Constrained Clustering: a Generalized Spectral Method
Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla et al.
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces
Amirali Abdullah, Ravi Kumar, Andrew McGregor et al.
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
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
Streaming Kernel Principal Component Analysis
Mina Ghashami, Daniel J. Perry, Jeff Phillips
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Mario Lucic, Olivier Bachem, Andreas Krause
Supervised Neighborhoods for Distributed Nonparametric Regression
Adam Bloniarz, Ameet Talwalkar, Bin Yu et al.
Survey Propagation beyond Constraint Satisfaction Problems
Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Anima Anandkumar, Prateek Jain, Yang Shi et al.
The Nonparametric Kernel Bayes Smoother
Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse et al.
Tightness of LP Relaxations for Almost Balanced Models
Adrian Weller, Mark Rowland, David Sontag
Tight Variational Bounds via Random Projections and I-Projections
Lun-Kai Hsu, Tudor Achim, Stefano Ermon
Time-Varying Gaussian Process Bandit Optimization
Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls
Kwang-Sung Jun, Kevin Jamieson, Robert Nowak et al.
Topic-Based Embeddings for Learning from Large Knowledge Graphs
Changwei Hu, Piyush Rai, Lawrence Carin
Towards Stability and Optimality in Stochastic Gradient Descent
Panos Toulis, Dustin Tran, Edo Airoldi
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
Fanhua Shang, Yuanyuan Liu, James Cheng
Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Cote, Nando Freitas
Universal Models of Multivariate Temporal Point Processes
Asela Gunawardana, Chris Meek
Unsupervised Ensemble Learning with Dependent Classifiers
Ariel Jaffe, Ethan Fetaya, Boaz Nadler et al.
Unsupervised Feature Selection by Preserving Stochastic Neighbors
Xiaokai Wei, Philip S. Yu