Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable approximation error. Recent work accounts for this error in the form of computational uncertainty, which enables--at the cost of quadratic complexity--an explicit tradeoff between computational efficiency and precision. Here we extend this development to model selection, which requires significant enhancements to the existing approach, including linear-time scaling in the size of the dataset. We propose a novel training loss for hyperparameter optimization and demonstrate empirically that the resulting method can outperform SGPR, CGGP and SVGP, state-of-the-art methods for GP model selection, on medium to large-scale datasets. Our experiments show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty-- a fundamental prerequisite for optimal decision-making.

In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

Our initial experiments (implementation, debugging, hyperparameter tuning, etc.) required about 5000CPUhoursofcompute. Due to these rules, it is recommended to group together in order to attack simultaneously. In Warehouse[4], QTRAN makes slightly faster progress than VAST(η = 12). The results forWarehouse[16], Battle[80], and GaussianSqueeze[800] are shown in Figure 1. Figure 10: Visualizations of the generated sub-teams ofXMetaGrad with η = 14 and XSpatial with k-means clustering using 10 centroids at different stages (early, middle, late) inBattle[80] after training. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.

Clustered attention makes use of similarities between queries and groups them in order to reduce the computational cost. In particular, we perform fast clustering using locality-sensitive hashing and K-Means and only compute the attention once per cluster.