Traditional process monitoring techniques provide dashboards and reports showing the recent performance of a business process in terms of key performance indicators such as mean execution time, resource utilization or error rate with respect to a given notion of error. Predictive (business) process monitoring techniques go beyond traditional ones by making predictions about the future state of the executions of a business process (herein called cases). For example, a predictive monitoring technique may seek to predict the remaining execution time of each ongoing case of a process [29], the next activity that will be executed in each case [11], or the final outcome of a case, with respect to a possible set of business outcomes [23-25]. For instance, in an order-to-cash process (a process going from the receipt of a purchase order to the receipt of payment of the corresponding invoice), the possible outcomes of a case may be that the purchase order is closed satisfactorily (i.e., the customer accepted the products and paid) or unsatisfactorily (e.g., the order was canceled or withdrawn). Another set of possible outcomes is that the products were delivered on time (with respect to a maximum acceptable delivery time), or delivered late. Recent years have seen the emergence of a rich field of proposed methods for predictive process monitoring in general, and predictive monitoring of (categorical) case outcomes in particular - herein called outcome-oriented predictive process monitoring. Unfortunately, there is no unified approach to evaluate these methods. Indeed, different authors have used different datasets, experimental settings, evaluation measures and baselines.

This survey presents the main results achieved for the influence maximization problem in social networks. This problem is well studied in the literature and, thanks to its recent applications, some of which currently deployed on the field, it is receiving more and more attention in the scientific community. The problem can be formulated as follows: given a graph, with each node having a certain probability of influencing its neighbors, select a subset of vertices so that the number of nodes in the network that are influenced is maximized. Starting from this model, we introduce the main theoretical developments and computational results that have been achieved, taking into account different diffusion models describing how the information spreads throughout the network, various ways in which the sources of information could be placed, and how to tackle the problem in the presence of uncertainties affecting the network. Finally, we present one of the main application that has been developed and deployed exploiting tools and techniques previously discussed.

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

To connect with potential customers, our marketers and sellers at Microsoft depend on good-quality leads. But sometimes people fill out online forms with fake names, gibberish, or even profanity. We distinguish fake company names from legitimate names in our data using the programming language R, the Naive Bayes classifier algorithm, Microsoft Machine Learning Server, and a data quality service that we built. This solution helps us weed out fake names and prioritize good leads for our sales and marketing teams.

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context. Overall, there is a lack of common testing practices. This paper thus presents a testbed for incremental non-stationary learning algorithms, based on specially designed synthetic datasets. Also, test results are reported for some well-known algorithms to show that the proposed methodology is effective at characterizing their strengths and weaknesses. It is expected that this methodology will provide a common basis for evaluating future contributions in the field.

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that map features to a high dimensional non-linear space. However, most of the basis function-based algorithms are time consuming. We propose an incremental sparse Bayesian approach to OR tasks and introduce an algorithm to sequentially learn the relevant basis functions in the ordinal scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression (ISBOR), automatically optimizes the hyper-parameters via the type-II maximum likelihood method. By exploiting fast marginal likelihood optimization, ISBOR can avoid big matrix inverses, which is the main bottleneck in applying basis function-based algorithms to OR tasks on large-scale datasets. We show that ISBOR can make accurate predictions with parsimonious basis functions while offering automatic estimates of the prediction uncertainty. Extensive experiments on synthetic and real word datasets demonstrate the efficiency and effectiveness of ISBOR compared to other basis function-based OR approaches.

Inverse reinforcement learning is the problem of inferring the reward function of an observed agent, given its policy or behavior. Researchers perceive IRL both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners in machine learning to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges which include accurate inference, generalizability, correctness of prior knowledge, and growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions of traditional IRL methods: (i) inaccurate and incomplete perception, (ii) incomplete model, (iii) multiple rewards, and (iv) non-linear reward functions. This discussion concludes with some broad advances in the research area and currently open research questions.

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.

Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.

We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We provide three mistake-bounded algorithms based on three paradigmatic methods for online algorithm design. The algorithm with the strongest guarantee is a quasi-Bayesian classifier which requires $\mathcal{O}(t \log n)$ time to predict at trial $t$ on an $n$-vertex graph. The fastest algorithm (with the weakest guarantee) is based on a specialist [10] approach and surprisingly only requires $\mathcal{O}(\log n)$ time on any trial $t$. We also give an algorithm based on a kernelized Perceptron with an intermediate per-trial time complexity of $\mathcal{O}(n)$ and a mistake bound which is not strictly comparable. Finally, we provide experiments on simulated data comparing these methods.