polynomial approximation

CipherPrune: Efficient and Scalable Private Transformer Inference ICLR 2025 Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived ICML 2025 Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding COLING 2024 Nimbus: Secure and Efficient Two-Party Inference for Transformers NeurIPS 2024 AutoReP: Automatic ReLU Replacement for Fast Private Network Inference ICCV 2023 Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees L4DC 2023 Learning Optimal Feedback Operators and their Sparse Polynomial Approximations JMLR 2023 On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy NeurIPS 2023 TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm ICML 2022 Efficient sampling from the Bingham distribution ALT 2021 Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations ICML 2020 Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy JMLR 2020 Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix ICML 2020 Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited NeurIPS 2018 Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance COLT 2018 Scaling the Poisson GLM to massive neural datasets through polynomial approximations NeurIPS 2018 Principal Component Projection Without Principal Component Analysis ICML 2016 Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016 Agnostic Learning of Disjunctions on Symmetric Distributions JMLR 2015 Distribution-independent Reliable Learning COLT 2014 Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching COLT 2013 Practical Large Scale Classification with Additive Kernels ACML 2012