Papers
Scalable Optimization of Neighbor Embedding for Visualization
Zhirong Yang, Jaakko Peltonen, Samuel Kaski
Scalable Simple Random Sampling and Stratified Sampling
Xiangrui Meng
Scale Invariant Conditional Dependence Measures
Sashank J Reddi, Barnabas Poczos
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations
Elad Gilboa, Yunus Saatçi, John Cunningham et al.
Scaling the Indian Buffet Process via Submodular Maximization
Colorado Reed, Ghahramani Zoubin
Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion
Jinfeng Yi, Lijun Zhang, Rong Jin et al.
Sequential Bayesian Search
Zheng Wen, Branislav Kveton, Brian Eriksson et al.
Sharp Generalization Error Bounds for Randomly-projected Classifiers
Robert Durrant, Ata Kaban
Smooth Operators
Steffen Grunewalder, Gretton Arthur, John Shawe-Taylor
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation
Sebastian Brechtel, Tobias Gindele, Rüdiger Dillmann
Sparse coding for multitask and transfer learning
Andreas Maurer, Massi Pontil, Bernardino Romera-Paredes
Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting
Matt Wytock, Zico Kolter
Sparse PCA through Low-rank Approximations
Dimitris Papailiopoulos, Alexandros Dimakis, Stavros Korokythakis
Sparse projections onto the simplex
Anastasios Kyrillidis, Stephen Becker, Volkan Cevher et al.
Sparse Uncorrelated Linear Discriminant Analysis
Xiaowei Zhang, Delin Chu
Sparsity-Based Generalization Bounds for Predictive Sparse Coding
Nishant Mehta, Alexander Gray
Spectral Compressed Sensing via Structured Matrix Completion
Yuxin Chen, Yuejie Chi
Spectral Experts for Estimating Mixtures of Linear Regressions
Arun Tejasvi Chaganty, Percy Liang
Spectral Learning of Hidden Markov Models from Dynamic and Static Data
Tzu-Kuo Huang, Jeff Schneider
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning
Gang Niu, Wittawat Jitkrittum, Bo Dai et al.
Stability and Hypothesis Transfer Learning
Ilja Kuzborskij, Francesco Orabona
Stable Coactive Learning via Perturbation
Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy et al.