Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference . Our framework gives exact (non-asymptotic) confidence intervals and p-values for the significance of the selected biclusters.

Generating good candidate actions is critical to the success of sample-based planners, particularly in continuous or large action spaces.

Neural Information Processing Systems http://nips.cc/

The problems of interest are often NP-complete and traditional methods efficient in practice usually rely on heuristics or produce approximate solutions.

Neural Information Processing Systems http://nips.cc/

The results on CIFAR10 were listed in Table R1. It reveals that HOGA searched by AutoLA (k=4)) still outperforms SE and CBAM by a large margin. We further customized SE and CBAM using the group split operation (denoted by "HOG"), resulting in a specific The HOGA searched by AutoLA outperforms its randomly search counterparts (denoted by "Rand"). We tested the generalization ability of HOGA searched on ResNet56 (denoted by "AutoLA_56") WiderResNet, indicating the consistent superiority of the HOGA searched by AutoLA over previous attention methods. We also compared AutoLA with SE and CBAM on a larger backbone (e.g., The results in Table R3 suggest that AutoLA still outperforms other attention modules.

This problem-dependent sample complexity result is expressed in terms of the sub-optimality gaps of the state-action pairs that are visited during exploration.

This problem-dependent sample complexity result is expressed in terms of the sub-optimality gaps of the state-action pairs that are visited during exploration.

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning.