In this paper, we study a generic learning framework for building robust text classification model that achieves accuracy comparable to standard full models under test-time budget constraints. Our approach learns a selector to identify words that are relevant to the prediction tasks and only passes these words to the classifier for processing. The selector is trained jointly with the classifier and directly learns to incorporate with the classifier. We further propose a data aggregation scheme to improve the robustness of the classifier. Our learning framework is general and can be incorporated with any type of text classification model. On real-world data, we show that the proposed approach improves the performance of a given classifier and speeds up the model with a mere loss in accuracy performance.

In order to exploit quantum advantages, quantum algorithms are indispensable for operating machine learning with quantum computers. We here propose an intriguing hybrid approach of quantum information processing for quantum linear regression, which utilizes both discrete and continuous quantum variables, in contrast to existing wisdoms based solely upon discrete qubits. In our framework, data information is encoded in a qubit system, while information processing is tackled using auxiliary continuous qumodes via qubit-qumode interactions. Moreover, it is also elaborated that finite squeezing is quite helpful for efficiently running the quantum algorithms in realistic setup. Comparing with an all-qubit approach, the present hybrid approach is more efficient and feasible for implementing quantum algorithms, still retaining exponential quantum speed-up.

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.

Heterogeneous treatment effects are the center of gravity in many modern causal inference applications. In this paper, we investigate the estimation and inference of heterogeneous treatment effects with precision in a general nonparametric setting. To this end, we enhance the classical $k$-nearest neighbor method with a simple algorithm, extend it to a distributional setting, and suggest the two-scale distributional nearest neighbors (DNN) estimator with reduced finite-sample bias. Our recipe is first to subsample the data and average the 1-nearest neighbor estimators from each subsample. With appropriately chosen subsampling scale, the resulting DNN estimator is proved to be asymptotically unbiased and normal under mild regularity conditions. We then proceed with combining DNN estimators with different subsampling scales to further reduce bias. Our theoretical results on the advantages of the new two-scale DNN framework are well supported by several Monte Carlo simulations. The newly suggested method is also applied to a real-life data set to study the heterogeneity of treatment effects of smoking on children's birth weights across mothers' ages.

This is the 5th article of series "Coding Deep Learning for Beginners". You will be able to find here links to all articles, agenda, and general information about an estimated release date of next articles on the bottom of the 1st article. They are also available in my open source portfolio -- MyRoadToAI, along with some mini-projects, presentations, tutorials and links. In this article, I will explain the concept of training Machine Learning algorithms with Gradient Descent. Majority of supervised algorithms are taking advantage of it -- especially all Neural Networks.

Rankings of universities and specialized academic programs have a major influence on students deciding what university to attend, faculty deciding where to work, government bodies deciding where and how to invest education and research funding, and university leaders deciding how to grow their institutions.9 There is general agreement in scientometrics that the quality of a university or a program depends on many factors, and different ranking metrics might be appropriate for different types of users. However, major points of contention emerge when it comes to agreeing on ranking methodology.20 Given the increasing impact of rankings, there is a need to better understand the actors influencing rankings and come up with a justifiable, transparent formula that encourages high-quality education and research at universities.11 We aim to contribute toward achieving this objective by focusing on ranking of the U.S. doctoral programs in computer science. We broadly group quality measures into objective (such as average research funding per faculty member) and subjective (such as peer assessment). The influential U.S. News ranking of computer science doctoral programsa is based purely on peer assessment in which computer science department chairs are asked to score other computer science programs on a scale of 1 to 5, with 1 being "marginal" and 5 being "outstanding," or enter "do not know" if not sufficiently familiar with the program. The final ranking is obtained by averaging the individual scores.

Things are about to get a little… wiggly. In contrast to the methods we've covered so far -- linear regression, logistic regression, and SVMs where the form of the model was pre-defined -- non-parametric learners do not have a model structure specified a priori. We don't speculate about the form of the function f that we're trying to learn before training the model, as we did previously with linear regression. Instead, the model structure is purely determined from the data. These models are more flexible to the shape of the training data, but this sometimes comes at the cost of interpretability.

This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python. You can also go through the Slides here: https://goo.gl/aNmKbQ Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-... #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people's digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data. As examples, we study sparse Gaussian mixture model, mixture of sparse linear regressions, and sparse phase retrieval model. For these models, we exploit an oracle-based computational model to establish conjecture-free computationally feasible minimax lower bounds, which quantify the minimum signal strength required for the existence of any algorithm that is both computationally tractable and statistically accurate. Our analysis shows that there exist significant gaps between computationally feasible minimax risks and classical ones. These gaps quantify the statistical price we must pay to achieve computational tractability in the presence of data heterogeneity. Our results cover the problems of detection, estimation, support recovery, and clustering, and moreover, resolve several conjectures of Azizyan et al. (2013, 2015); Verzelen and Arias-Castro (2017); Cai et al. (2016). Interestingly, our results reveal a new but counter-intuitive phenomenon in heterogeneous data analysis that more data might lead to less computation complexity.

I am exploring these ideas in a free coding workshop/ meetups in London. There is already a waiting list. We may hold more workshops next year depending on how these go. I have been involved in teaching Data Science for a few years now (Oxford University - Data Science for Internet of Things and also online). Over the years, I have tried to improve my teaching .. and adopt ideas from other domains into my teaching One such technique is Deliberate practice a technique which probably originated in the former Soviet Union to train world class athletes.