In late March, a judge in Wiesbaden, Germany, found herself playing the uncomfortable role of art critic. On trial before her were two men accused of forging paintings by artists including Kazimir Malevich and Wassily Kandinsky, whose angular, abstract compositions can now go for eight-figure prices. The case had been in progress for three and a half years and was seen by many as a test. A successful prosecution could help end an epidemic of forgeries – so-called miracle pictures that appear from nowhere – that have been plaguing the market in avant-garde Russian art. But as the trial reached its climax, it disintegrated into farce.

While it is relatively easy to start an online advertising campaign, obtaining a high Key Performance Indicator (KPI) can be challenging. A large body of work on this subject has already been performed and platforms known as DSPs are available on the market that deal with such an optimization. From the advertiser's point of view, each DSP is a different black box, with its pros and cons, that needs to be configured. In order to take advantage of the pros of every DSP, advertisers are well-advised to use a combination of them when setting up their campaigns. In this paper, we propose an algorithm for advertisers to add an optimization layer on top of DSPs. The algorithm we introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the DSPs and putting them in competition with each other. SKOTT is a highly specialized iterative algorithm loosely based on gradient descent that is made up of three independent sub-routines, each dealing with a different problem: partitioning the budget, setting the desired average bid, and preventing under-delivery. In particular, one of the novelties of our approach lies in our taking the perspective of the advertisers rather than the DSPs. Synthetic market data is used to evaluate the efficiency of SKOTT against other state-of-the-art approaches adapted from similar problems. The results illustrate the benefits of our proposals, which greatly outperforms the other methods.

Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate. The sensitivity to the learning rate is due to the reliance on backpropagation to train the network. In this paper we present the first application of Implicit Stochastic Gradient Descent (ISGD) to train neural networks, a method known in convex optimization to be unconditionally stable and robust to the learning rate. Our key contribution is a novel layer-wise approximation of ISGD which makes its updates tractable for neural networks. Experiments show that our method is more robust to high learning rates and generally outperforms standard backpropagation on a variety of tasks.

Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, these assumptions have focused on estimation in a single causal problem. In this work, we develop techniques for causal estimation in causal problems with multiple treatments. We develop two assumptions based on shared confounding between treatments and independence of treatments given the confounder. Together these assumptions lead to a confounder estimator regularized by mutual information. For this estimator, we develop a tractable lower bound. To fit the outcome model, we use the residual information in the treatments given the confounder.

Despite great advances, molecular cancer pathology is often limited to use a small number of biomarkers rather than the whole transcriptome, partly due to the computational challenges. Here, we introduce a novel architecture of DNNs that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. We employed this architecture on mRNA transcription profiles of 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our system significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. Furthermore, it can predict miRNA transcription profile of each sample, which enables performing miRNA expression research when only mRNA transcriptome data are available. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research.

Conventional approaches used supervised learning to estimate off-line writer identifications. In this study, we improved the off-line writer identifica- tions by semi-supervised feature learning pipeline, which trained the extra unla- beled data and the original labeled data simultaneously. In specific, we proposed a weighted label smoothing regularization (WLSR) method, which assigned the weighted uniform label distribution to the extra unlabeled data. We regularized the convolutional neural network (CNN) baseline, which allows learning more discriminative features to represent the properties of different writing styles. Based on experiments on ICDAR2013, CVL and IAM benchmark datasets, our results showed that semi-supervised feature learning improved the baseline meas- urement and achieved better performance compared with existing writer identifications approaches.

With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates to inference accuracy and efficiency to steer the improved design of future DNN platforms. We show that inference using these custom numeric representations on production-grade DNNs, including GoogLeNet and VGG, achieves an average speedup of 7.6x with less than 1% degradation in inference accuracy relative to a state-of-the-art baseline platform representing the most sophisticated hardware using single-precision floating point. To facilitate the use of such customized precision, we also present a novel technique that drastically reduces the time required to derive the optimal precision configuration.

This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.

Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.

The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.