In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and randomly flips the sign of each entry of the resulting vector with probability 1/2. A salient question is whether this scheme is secure in any provable sense, perhaps under a plausible hardness assumption and assuming the distributions generating the public and private data satisfy certain properties. We show that the answer to this appears to be quite subtle and closely related to the average-case complexity of a new multi-task, missing-data version of the classic problem of phase retrieval. Motivated by this connection, we design a provable algorithm that can recover private vectors using only the public vectors and synthetic vectors generated by InstaHide, under the assumption that the private and public vectors are isotropic Gaussian.

Multi-view datasets are increasingly collected in many real-world applications, and we have seen better learning performance by existing multi-view learning methods than by conventional single-view learning methods applied to each view individually. But, most of these multi-view learning methods are built on the assumption that at each instance no view is missing and all data points from all views must be perfectly paired. Hence they cannot handle unpaired data but ignore them completely from their learning process. However, unpaired data can be more abundant in reality than paired ones and simply ignoring all unpaired data incur tremendous waste in resources. In this paper, we focus on learning uncorrelated features by semi-paired subspace learning, motivated by many existing works that show great successes of learning uncorrelated features. Specifically, we propose a generalized uncorrelated multi-view subspace learning framework, which can naturally integrate many proven learning criteria on the semi-paired data. To showcase the flexibility of the framework, we instantiate five new semi-paired models for both unsupervised and semi-supervised learning. We also design a successive alternating approximation (SAA) method to solve the resulting optimization problem and the method can be combined with the powerful Krylov subspace projection technique if needed. Extensive experimental results on multi-view feature extraction and multi-modality classification show that our proposed models perform competitively to or better than the baselines.

Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required. We present a novel online algorithm: Online Orthogonal Matching Pursuit (OOMP) for online support recovery in the random design setting of sparse linear regression. Our procedure selects features sequentially, alternating between allocation of samples only as needed to candidate features, and optimization over the selected set of variables to estimate the regression coefficients. Theoretical guarantees about the output of this algorithm are proven and its computational complexity is analysed.

The model prediction of the Gaussian process (GP) regression can be significantly biased when the data are contaminated by outliers. We propose a new robust GP regression algorithm that iteratively trims a portion of the data points with the largest deviation from the predicted mean. While the new algorithm retains the attractive properties of the standard GP as a nonparametric and flexible regression method, it can significantly reduce the influence of outliers even in some extreme cases. It is also easier to implement than previous robust GP variants that rely on approximate inference. Applied to various synthetic datasets with contaminations, the proposed method outperforms the standard GP and the popular robust GP variant with the Student's t likelihood, especially when the outlier fraction is high. Lastly, as a practical example in the astrophysical study, we show that this method can determine the main-sequence ridge line precisely in the color-magnitude diagram of star clusters.

As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensitive to outliers, which has been improved by numerous Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density, which is improved for large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers. Extensive experiments on both synthetic and real-world datasets with vector-level or pixel-level outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art RPCA based methods.

Fraud detection is the most important step for a risk management process to prevent a recurrence. High volumes of fraud can be damaging revenue and reputation. Fortunately, it is possible to deal with fraud before it happens. Therefore, I would like to investigate the performance of the machine learning algorithms on a credit card fraud data set. The dataset contains transactions made by credit cards in September 2013 by European cardholders.

It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly. I will start with some general functions and show some more topics using the Facebook Stock price dataset. Time series data can come in with so many different formats. But not all of those formats are friendly to python's pandas' library. The most convenient format is the timestamp format for Pandas.

Logistic Regression is the most confusing unsupervised Machine learning Algorithm. As it is a Classification type Algorithm, the word Regression in its naming often tends to confuse the newbies in the Data Science world. We know that Linear Regression is used to predict the output, which consists of continuous values. However, imagine a situation if we want to classify our continuous outputs into various classes. E.g., if we are data of marks of students and based on the percentage output, we want to classify whether it's pass or fail.

Online Courses Udemy - Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications BESTSELLER Created by Lazy Programmer Team, Lazy Programmer Inc English [Auto-generated], French [Auto-generated], 4 more Students also bought Data Science: Natural Language Processing (NLP) in Python Natural Language Processing with Deep Learning in Python Deep Learning Prerequisites: Linear Regression in Python Cluster Analysis and Unsupervised Machine Learning in Python Complete Python Bootcamp: Go from zero to hero in Python3 Preview this course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

K-Means Algorithm seeks to find K number of clusters in a data set. This clusters have to be apart as they can be from each other and keep their elements as closely as possible. Cluster analysis is ideal to find patterns, client segmentation, and in our case, to find any similitude. However, the question is always the same: what is the K numbers that makes the number of clusters optimal?