Identification-robust inference for the LATE with high-dimensional covariates

This paper investigates the local average treatment effect (LATE) with high-dimensional covariates, irrespective of the strength of identification. We propose a novel test statistic for the high-dimensional LATE and demonstrate that our test has uniformly correct asymptotic size. By employing the double/debiased machine learning method for nuisance parameter estimation, we develop easy-to-implement algorithms for inference and confidence interval calculation of the high-dimensional LATE. Simulations indicate that our test is robust against both weak identification and high dimensionality concerning size control and power performance, outperforming other conventional tests. Applying the proposed method to railroad and population data to study the effect of railroad access on urban population growth, we observe that our methodology yields point estimates for the railroad access coefficients that are smaller in magnitude and confidence intervals that are by 49% to 92% shorter depending on specifications, when compared to the conventional results.Comment: 52pages, 2 figure

Few-shot classification in Named Entity Recognition Task

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.Comment: In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computin

Deep point-based scene labeling with depth mapping and geometric patch feature encoding

This paper presents a deep CNN approach for point-based semantic scene labeling. This is challenging because 3D point clouds do not have a canonical domain and can have complex geometry and substantial variation of sampling densities. We propose a novel framework where the convolution operator is defined on depth maps around sampled points, which captures characteristics of local surface regions. We introduce Depth Mapping (DM) and Reverse Depth Mapping (RDM) operators to transform between the point domain and the depth map domain. Our depth map based convolution is computationally efficient, robust to scene scales and sampling densities, and can capture rich surface characteristics. We further propose to augment each point with feature encoding of the local geometric patches resulted from multi-method through patch pooling network (PPN). The patch features provide complementary information and are fed into our classification network to achieve semantic segmentation