approximation error

A determinantal point process for column subset selection JMLR 2020 Better depth-width trade-offs for neural networks through the lens of dynamical systems ICML 2020 Data-Independent Neural Pruning via Coresets ICLR 2020 Harmonic Decompositions of Convolutional Networks ICML 2020 Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? ICLR 2020 On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis AISTATS 2020 Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds NeurIPS 2019 Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability AISTATS 2019 Theoretical Analysis of Label Distribution Learning AAAI 2019 Memory Efficient Kernel Approximation JMLR 2017 On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions JMLR 2017 Unifying Task Specification in Reinforcement Learning ICML 2017 Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness JMLR 2016 Synthesis of MCMC and Belief Propagation NeurIPS 2016 Basis refinement strategies for linear value function approximation in MDPs NeurIPS 2015 Bounding errors of Expectation-Propagation NeurIPS 2015 Column Selection via Adaptive Sampling NeurIPS 2015 On Flat versus Hierarchical Classification in Large-Scale Taxonomies NeurIPS 2013 Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression AISTATS 2012 Multiclass Learning Approaches: A Theoretical Comparison with Implications NeurIPS 2012 Expressive Power and Approximation Errors of Restricted Boltzmann Machines NeurIPS 2011 Shallow vs. Deep Sum-Product Networks NeurIPS 2011 Error Propagation for Approximate Policy and Value Iteration NeurIPS 2010 Biasing Approximate Dynamic Programming with a Lower Discount Factor NeurIPS 2008