high-dimensional optimization

When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 High-dimensional Bayesian optimization with sparse axis-aligned subspaces UAI 2021 High Dimensional Level Set Estimation with Bayesian Neural Network AAAI 2021 Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces ICML 2021 Do Subsampled Newton Methods Work for High-Dimensional Data? AAAI 2020 Efficient and Robust High-Dimensional Linear Contextual Bandits IJCAI 2020 Explicit Gradient Learning for Black-Box Optimization ICML 2020 High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds NeurIPS 2020 Model Inversion Networks for Model-Based Optimization NeurIPS 2020 Projective Preferential Bayesian Optimization ICML 2020 Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization NeurIPS 2020 Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization AAAI 2020 A Framework for Bayesian Optimization in Embedded Subspaces ICML 2019 Dynamic Pricing in High-dimensions JMLR 2019 High Dimensional Bayesian Optimization via Supervised Dimension Reduction IJCAI 2019 High-Dimensional Optimization in Adaptive Random Subspaces NeurIPS 2019 Large Scale Structure of Neural Network Loss Landscapes NeurIPS 2019 Scalable Global Optimization via Local Bayesian Optimization NeurIPS 2019 Batched Large-scale Bayesian Optimization in High-dimensional Spaces AISTATS 2018 BOCK : Bayesian Optimization with Cylindrical Kernels ICML 2018 Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features NeurIPS 2018 Gradient Estimation with Simultaneous Perturbation and Compressive Sensing JMLR 2018 High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups AISTATS 2018 Stochastic Zeroth-order Optimization in High Dimensions AISTATS 2018 Batched High-dimensional Bayesian Optimization via Structural Kernel Learning ICML 2017