Representation Learning

An Analysis of Single-Layer Networks in Unsupervised Feature Learning AISTATS 2011 A Non-Parametric Approach to Dynamic Programming NeurIPS 2011 A novel greedy algorithm for Nyström approximation AISTATS 2011 A reinterpretation of the policy oscillation phenomenon in approximate policy iteration NeurIPS 2011 A Serial Approach to Handling High-Dimensional Measurements in the Sigma-Point Kalman Filter RSS 2011 A Spike and Slab Restricted Boltzmann Machine AISTATS 2011 Bayesian Spike-Triggered Covariance Analysis NeurIPS 2011 Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information ACML 2011 Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection AISTATS 2011 Convex and Network Flow Optimization for Structured Sparsity JMLR 2011 Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs JMLR 2011 Data Skeletonization via Reeb Graphs NeurIPS 2011 Deep Learners Benefit More from Out-of-Distribution Examples AISTATS 2011 Deep Sparse Rectifier Neural Networks AISTATS 2011 Demixed Principal Component Analysis NeurIPS 2011 Dimensionality Reduction for Spectral Clustering AISTATS 2011 Dimensionality Reduction Using the Sparse Linear Model NeurIPS 2011 Discussion of “Learning Scale Free Networks by Reweighted ℓ_1 regularization” AISTATS 2011 Discussion of “The Neural Autoregressive Distribution Estimator” AISTATS 2011 Divide-and-Conquer Matrix Factorization NeurIPS 2011 Efficient anomaly detection using bipartite k-NN graphs NeurIPS 2011 Efficient inference in matrix-variate Gaussian models with \iid observation noise NeurIPS 2011 Efficient Structure Learning of Bayesian Networks using Constraints JMLR 2011 EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning NeurIPS 2011 Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning AISTATS 2011