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Found 9 papers, 6 papers with code
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation.
Policy Gradient with Kernel Quadrature
Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement learning tasks.
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation.
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
To address this problem, we develop a quantum ridgelet transform (QRT), which implements the ridgelet transform of a quantum state within a linear runtime $O(D)$ of quantum computation.
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel.
Sampling-based Nyström Approximation and Kernel Quadrature
We analyze the Nystr\"om approximation of a positive definite kernel associated with a probability measure.
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.
Positively Weighted Kernel Quadrature via Subsampling
We study kernel quadrature rules with convex weights.
On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
Whereas existing theoretical studies of deep learning have been based mainly on mathematical theories of well-known function classes such as H\"{o}lder and Besov classes, we focus on function classes with discontinuity and sparsity, which are those naturally assumed in practice.
