Learning with pairwise ranking methods for implicit feedback datasets has shown promising results as compared to pointwise ranking methods for recommendation tasks. However, there is limited effort in scaling the pairwise ranking methods in a large scale distributed setting. In this paper we address the scalability aspect of a pairwise ranking method using Factorization Machines in distributed settings. Our proposed method is based on a block partitioning of the model parameters so that each distributed worker runs stochastic gradient updates on an independent block. We developed a dynamic block creation and exchange strategy by utilizing the frequency of occurrence of a feature in the local training data of a worker. Empirical evidence on publicly available benchmark datasets indicates that the proposed method scales better than the static block based methods and outperforms competing state-of-the-art methods.

Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners. Therefore, it is not surprising that automated machine learning has become a very interesting field of research. While current research is mainly focusing on finding good pairs of algorithms and hyperparameter configurations, we will present an approach that automates the process of creating a top performing ensemble of several layers, different algorithms and hyperparameter configurations. These kinds of ensembles are called jokingly Frankenstein ensembles and proved their benefit on versatile data sets in many machine learning challenges. We compare our approach Automatic Frankensteining with the current state of the art for automated machine learning on 80 different data sets and can show that it outperforms them on the majority using the same training time. Furthermore, we compare Automatic Frankensteining on a large scale data set to more than 3,500 machine learning expert teams and are able to outperform more than 3,000 of them within 12 CPU hours.

Previous research on content-based geolocation in general has developed prediction methods via conducting pre-partitioning and applying classification methods. The input of these methods is the concatenation of individual tweets during a period of time. But unfortunately, these methods have some drawbacks. They discard the natural real-values properties of latitude and longitude as well as fail to capture geolocation in near real-time. In this work, we develop a novel generative content-based regression model via a matrix factorization technique to tackle the near real-time geolocation prediction problem. With this model, we aim to address a couple of un-answered questions. First, we prove that near real-time geolocation prediction can be accomplished if we leave out the concatenation. Second, we account the real-values properties of physical coordinates within a regression solution. We apply our model on Twitter datasets as an example to prove the effectiveness and generality. Our experimental results show that the proposed model, in the best scenario, outperforms a set of state-of-the-art regression models including Support Vector Machines and Factorization Machines by a reduction of the median localization error up to 79%.

Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter configurations. Originally designed for black-box optimization, researchers have contributed different meta-learning approaches to speed up the optimization process. We create a generalized framework of SMBO and its recent additions which gives access to adaptive hyperparameter transfer learning with simple surrogates (AHT), a new class of hyperparameter optimization strategies. AHT provides less time-overhead for the optimization process by replacing time-and space-consuming transfer surrogate models with simple surrogates that employ adaptive transfer learning. In an empirical comparison on two different meta-data sets, we can show that AHT outperforms various instances of the SMBO framework in the scenarios of hyperparameter tuning and algorithm selection.

Motifs are the most repetitive/frequent patterns of a time-series. The discovery of motifs is crucial for practitioners in order to understand and interpret the phenomena occurring in sequential data. Currently, motifs are searched among series sub-sequences, aiming at selecting the most frequently occurring ones. Search-based methods, which try out series sub-sequence as motif candidates, are currently believed to be the best methods in finding the most frequent patterns. However, this paper proposes an entirely new perspective in finding motifs. We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs. We treat the occurrence frequency as a function and time-series motifs as its parameters, therefore we learn the optimal motifs that maximize the frequency function. In contrast to searching, our method is able to discover the most repetitive patterns (hence optimal), even in cases where they do not explicitly occur as sub-sequences. Experiments on several real-life time-series datasets show that the motifs found by our method are highly more frequent than the ones found through searching, for exactly the same distance threshold.

Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models. Currently, transferring knowledge from previous experiments to a new experiment is of particular interest because it has been shown that it allows to further improve the hyperparameter optimization. We propose to transfer knowledge by means of an initialization strategy for hyperparameter optimization. In contrast to the current state of the art initialization strategies, our strategy is neither limited to hyperparameter configurations that have been evaluated on previous experiments nor does it need meta-features. The initial hyperparameter configurations are derived by optimizing for a meta-loss formally defined in this paper. This loss depends on the hyperparameter response function of the data sets that were investigated in past experiments. Since this function is unknown and only few observations are given, the meta-loss is not differentiable. We propose to approximate the response function by a differentiable plug-in estimator. Then, we are able to learn the initial hyperparameter configuration sequence by applying gradient-based optimization techniques. Extensive experiments are conducted on two meta-data sets. Our initialization strategy is compared to the state of the art for initialization strategies and further methods that are able to transfer knowledge between data sets. We give empirical evidence that our work provides an improvement over the state of the art.

Hyperparameter tuning is often done manually but current research has proven that automatic tuning yields effective hyperparameter configurations even faster and does not require any expertise. To further improve the search, recent publications propose transferring knowledge from previous experiments to new experiments. We adapt the sequential model-based optimization by replacing its surrogate model and acquisition function with one policy that is optimized for the task of hyperparameter tuning. This policy generalizes over previous experiments but neither uses a model nor uses meta-features, nevertheless, outperforms the state of the art. We show that a static ranking of hyperparameter combinations yields competitive results and substantially outperforms a random hyperparameter search. Thus, it is a fast and easy alternative to complex hyperparameter tuning strategies and allows practitioners to tune their hyperparameters by simply using a look-up table. We made look-up tables for two classifiers publicly available: SVM and AdaBoost. Furthermore, we propose a similarity measure for data sets that yields more comprehensible results than those using meta-features. We show how this similarity measure can be applied to surrogate models in the SMBO framework and empirically show that this change leads to better hyperparameter configurations in less trials.

Recent work has demonstrated that hyperparameter optimization within the sequential model-based optimization (SMBO) framework is generally possible. This approach replaces the expensive-to-evaluate function that maps hyperparameters to the performance of a learned model on validation data by a surrogate model which is much cheaper to evaluate. The current state of the art in hyperparameter optimization learns these surrogate models across a variety of solved data sets where a grid search has already been employed. In this way, surrogate models are learned across data sets, and thus able to generalize better. However, meta features that describe characteristics of a data set are usually needed in order for the surrogate model to differentiate between same hyperparameter configurations on different data sets. Another research area that is closely related focuses on model choice, i.e. picking the right model for a given task, which is also a problem that many practitioners face in machine learning. In this paper, we aim to solve both of these problems with a unified surrogate model that learns across different data sets, different classifiers and their respective hyperparameters. We employ factorized multilayer perceptrons, a surrogate model that consists of a multilayer perceptron architecture, but offers the prediction of a factorization machine in the first layer. In this way, data sets, models and hyperparameters are being represented in a joint lower dimensional latent feature space. Experiments on a publicly available meta data set containing 59 individual data sets and 19 prediction models demonstrate the efficiency of our approach.

Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.