The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of the Euclidean metric are given.

Since worst-case statistical inference problems are often intractable (i.e., without distributional

Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the "last mile" to enhance the efficiency of object detection.

It solves distribution-valued supervised learning, where the output values of the training dataset are probability distributions.

Accordingly, extensive evaluations demonstrate the significance of our proposal and its scalability to multimodal large models in boosting robustness.

Matrix sketching is a powerful tool for reducing the size of large data matrices.

Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of computationally efficient data-driven algorithms for combinatorial algorithm families with multiple parameters.

Another recent contribution is that of Grave et al. [2019], where a method is proposed to