In machine learning, the notion of multi-armed bandits (MAB) refers to a class of online learning problems, in which a learner is supposed to simultaneously explore and exploit a given set of choice alternatives (metaphorically referred to as "arms") in the course of a sequential decision process (Lattimore and Szepesvári, 2019). In this paper, we consider an extension of the basic setting, which is practically motivated by the problem of preselection as recently introduced by Saha and Gopalan (2018b) and Bengs and Hüllermeier (2019): Instead of selecting a single arm, the learner is only supposed to preselect a promising subset of arms. The final choice is then made by a selector, for example a human user or another algorithm. In information retrieval, for instance, the role of the learner is played by a search engine, and the selector is the user who seeks a certain information. Another application, which served as a concrete motivation of our setting and will also be used in our experimental study, is the problem of algorithm (pre-)selection (Kerschke et al., 2018).

Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks are sampled randomly and their relationships are ignored. In this paper, we argue that the inter-meta-task relationships should be exploited to learn models that are more generalizable to unseen classes with few-shots. Specifically, we consider the relationships between two types of meta-tasks and propose different strategies to exploit them. (1) Two meta-tasks with disjoint sets of classes: these are interesting because their relationship is reminiscent of that between the source seen classes and target unseen classes, featured with domain gap caused by class differences. A novel meta-training strategy named meta-domain adaptation (MDA) is proposed to make the meta-learned model more robust to the domain gap. (2) Two meta-tasks with identical sets of classes: these are interesting because they can be used to learn models that are robust against poorly sampled few-shots. To that end, a novel meta-knowledge distillation (MKD) strategy is formulated. Extensive experiments demonstrate that both MDA and MKD significantly boost the performance of a variety of existing FSL methods and thus achieve new state-of-the-art on three benchmarks.

Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, achieving satisfactory accuracy of interpretable models require more time-consuming work related to feature engineering. Can we train interpretable and accurate models, without timeless feature engineering? We propose a method that uses elastic black-boxes as surrogate models to create a simpler, less opaque, yet still accurate and interpretable glass-box models. New models are created on newly engineered features extracted with the help of a surrogate model. We supply the analysis by a large-scale benchmark on several tabular data sets from the OpenML database. There are two results 1) extracting information from complex models may improve the performance of linear models, 2) questioning a common myth that complex machine learning models outperform linear models.

Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper, we propose a modification of the PGD method for adversarial training and demonstrate that models can be trained much more efficiently without any loss in accuracy on natural and adversarial samples. We argue that the initial phase of adversarial training is redundant and can be replaced with natural training thereby increasing the computational efficiency significantly. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time to up to 38\% of the original training time with comparable model accuracy and generalization on various strengths of adversarial attacks.

The simplified series is obtained by first applying their univariate technique to each of the variables independently; that is, each univariate batch of data is separated into variable-length subsequences, and the obtained subsequences are then clustered as explained in Section 4.1. With this process, a set of representative univariate subsequences is obtained for each variable. Each new multivariate batch of data is then represented by a vector of distances, (d 1,d 2,...,d l), where d j represents the Euclidean distance between the j th variable-length subsequence of the new batch and its corresponding representative subsequence. As with their univariate technique, the reference of normality that is considered by this method is the same time series. The technique proposed by Hu et al. [2019] is also based on reducing the dimensionality of the time series and allows us to detect variable-length discords, while using the same time series as the reference of normality. This is based on the fact that the most unusual subsequences tend to have local regions with significantly different densities (points that are similar) in comparison to the other subsequences in the series. Each point in the new univariate time series describes the density of a local region of the input multivariate time series obtained by a sliding window. This series is also used to obtain the variable-length subsequences. Discords are identified using the Euclidean and Bhattacharyya distances simultaneously.

Existing uncertainty modeling approaches try to detect an out-of-distribution point from the in-distribution dataset. We extend this argument to detect finer-grained uncertainty that distinguishes between (a). certain points, (b). uncertain points but within the data distribution, and (c). out-of-distribution points. Our method corrects overconfident NN decisions, detects outlier points and learns to say ``I don't know'' when uncertain about a critical point between the top two predictions. In addition, we provide a mechanism to quantify class distributions overlap in the decision manifold and investigate its implications in model interpretability. Our method is two-step: in the first step, the proposed method builds a class distribution using Kernel Activation Vectors (kav) extracted from the Network. In the second step, the algorithm determines the confidence of a test point by a hierarchical decision rule based on the chi-squared distribution of squared Mahalanobis distances. Our method sits on top of a given Neural Network, requires a single scan of training data to estimate class distribution statistics, and is highly scalable to deep networks and wider pre-softmax layer. As a positive side effect, our method helps to prevent adversarial attacks without requiring any additional training. It is directly achieved when the Softmax layer is substituted by our robust uncertainty layer at the evaluation phase.

We present an interpretable companion model for any pre-trained black-box classifiers. The idea is that for any input, a user can decide to either receive a prediction from the black-box model, with high accuracy but no explanations, or employ a companion rule to obtain an interpretable prediction with slightly lower accuracy. The companion model is trained from data and the predictions of the black-box model, with the objective combining area under the transparency--accuracy curve and model complexity. Our model provides flexible choices for practitioners who face the dilemma of choosing between always using interpretable models and always using black-box models for a predictive task, so users can, for any given input, take a step back to resort to an interpretable prediction if they find the predictive performance satisfying, or stick to the black-box model if the rules are unsatisfying. To show the value of companion models, we design a human evaluation on more than a hundred people to investigate the tolerable accuracy loss to gain interpretability for humans.

Let's quickly look at types of clustering algorithms and when you should choose each type. When choosing a clustering algorithm, you should consider whether the algorithm scales to your dataset. Datasets in machine learning can have millions of examples, but not all clustering algorithms scale efficiently. Many clustering algorithms work by computing the similarity between all pairs of examples. This means their runtime increases as the square of the number of examples \(n\), denoted as \(O(n 2)\) in complexity notation.

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Unsupervised algorithms make inferences from data sets using only input vectors without referring to known, or labeled, outcomes. Unsupervised learning allows us to approach problems with little or no idea what our results should look like. Unsupervised algorithms find patterns based only on input data. The technique is useful when we're not quite sure what to look for.

I am avoiding repeating same models with Python but included linear regression & logistic regression for continuation purpose. Going forward, I will cover other techniques with Python like image recognition, sentiment analysis etc. Image recognition is in progress & course will be updated soon with it. Unlike most machine learning courses out there, the Complete Machine Learning & Data Science with R-2019 is comprehensive. We are not only covering popular machine learning techniques but also additional techniques like ANOVA & CART techniques. Course is structured into various parts like R programming, data selection & manipulation, applied statistics & data visualization. This will help you with the structure of data science and machine learning.