We study the problem of estimating the parameters of a Boolean product distribution in $d$ dimensions, when the samples are truncated by a set $S \subset \{0, 1\}^d$ accessible through a membership oracle. This is the first time that the computational and statistical complexity of learning from truncated samples is considered in a discrete setting. We introduce a natural notion of fatness of the truncation set $S$, under which truncated samples reveal enough information about the true distribution. We show that if the truncation set is sufficiently fat, samples from the true distribution can be generated from truncated samples. A stunning consequence is that virtually any statistical task (e.g., learning in total variation distance, parameter estimation, uniformity or identity testing) that can be performed efficiently for Boolean product distributions, can also be performed from truncated samples, with a small increase in sample complexity. We generalize our approach to ranking distributions over $d$ alternatives, where we show how fatness implies efficient parameter estimation of Mallows models from truncated samples. Exploring the limits of learning discrete models from truncated samples, we identify three natural conditions that are necessary for efficient identifiability: (i) the truncation set $S$ should be rich enough; (ii) $S$ should be accessible through membership queries; and (iii) the truncation by $S$ should leave enough randomness in all directions. By carefully adapting the Stochastic Gradient Descent approach of (Daskalakis et al., FOCS 2018), we show that these conditions are also sufficient for efficient learning of truncated Boolean product distributions.

Three companies – Samsung, IBM and Tencent – dominate the global AI patent race over the past 10 years, while fierce competition between the U.S, and China overshadows other countries and regions, including the EU. These are the key findings of OxFirst, a specialist in IP law and economics (and spin out of Oxford University), which also reported that multiple neural nets, machine learning and speech recognition are driving the market. "Patents are mainly filed in the area of interconnectivity and system architecture, suggesting that top players focus primarily on protecting technologies covering multiple neural nets," OxFirst said in its announcement today. "Other areas of crucial importance are ML and bootstrap methods, alongside procedures used during speech recognition processes; e.g. the further establishment of human-machine dialogue." OxFirst said its sector-specific analysis suggests that major companies have focused on AI in the medical space, particularly medical diagnosis, medical simulation and data mining.

Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management. Due to increasingly large volumes of data sets being generated every minute, deep learning methods have been used extensively in the latest years for both short and long term prediction. However, such models, despite their efficiency, need large amounts of historical information to be provided, and they take a considerable amount of time and computing resources to train, validate and test. This paper presents two new spatial-temporal approaches for building accurate short-term prediction along a popular motorway in Sydney, by making use of the graph structure of the motorway network (including exits and entries). The methods are built on proximity-based approaches, denoted backtracking and interpolation, which uses the most recent and closest traffic flow information for each of the target counting stations along the motorway. The results indicate that for short-term predictions (less than 10 minutes into the future), the proposed graph-based approaches outperform state-of-the-art deep learning models, such as long-term short memory, convolutional neuronal networks or hybrid models.

This paper proposes a programmable relation extraction method for the English language by parsing texts into semantic graphs. A person can define rules in plain English that act as matching patterns onto the graph representation. These rules are designed to capture the semantic content of the documents, allowing for flexibility and ad-hoc entities. Relation extraction is a complex task that typically requires sizeable training corpora. The method proposed here is ideal for extracting specialized ontologies in a limited collection of documents.

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without directly interactive with training examples, the generative model can be fully used to estimate the underlying distribution of an original dataset while the discriminative model can examine the quality of the generated samples by comparing the label values with the training examples. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. To mitigate this information leakage and construct a private GAN, in this work we propose a R\'enyi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training. Moreover, we derive the analytical results of the total privacy loss under the subsampling method and cumulated iterations, which show its effectiveness on the privacy budget allocation. In addition, in order to mitigate the negative impact brought by the injecting noise, we enhance the proposed algorithm by adding an adaptive noise tuning step, which will change the volume of added noise according to the testing accuracy. Through extensive experimental results, we verify that the proposed algorithm can achieve a better privacy level while producing high-quality samples compared with a benchmark DP-GAN scheme based on noise perturbation on training gradients.

As many parts of the world continue to remain in lockdown due to the global pandemic, many countries have started to ease the restrictions. Particularly in Australia & New Zealand, schools have reopened, workers are heading back to their offices, and restaurants & retail stores are beginning to resume trade with new set of guidelines. These guidelines might also vary from one state to another and hence businesses that operate and have offices in different states, need to provide relevant updates to their employees to be able to comply with the new regulations. The most common practice from employers is to send out regular emails outlining the guidelines. Chatbots are beginning to play a vital role in providing real-time upto date information.

A McKinsey survey looking at the banking, auto insurance, retail energy, health insurance, and mobile communications sectors found that the quality and availability of digital interactions have a significant impact on customer satisfaction. Adding digital offerings is crucial to what consumer-facing companies must do to remain competitive in the face of increased customer expectations. However, some organisations still only offer basic digital services, and not all have created integrated, omnichannel experiences. Companies that use technology to transform customer experience have increased customer satisfaction by 15 to 20%, reducing cost to serve by 20 to 40%, and boosting conversion rates and growth by 20%. As consumers have come to expect the same experience of their financial services providers that they have elsewhere in their lives, traditional financial institutions (FIs) are increasingly looking for ways to improve customer service and deepen engagement.

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering such collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent's performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems.

Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. Given learners' exercise records, a knowledge tracing model can trace their hidden knowledge state dynamically. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, they still have limitations. Most existing methods simplify the exercising records as knowledge sequence, which fails to explore rich information existed in exercise texts. Besides, the latent hierarchical graph nature of exercises and knowledge remain unexplored. Thus, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which could leverage the advantages of hierarchical exercise graph and sequence model to enhance the ability of knowledge tracing. Besides, we introduce the concept of problem schema to better represent a group of similar exercises and propose a hierarchical graph neural network to learn representations of problem schemas. Moreover, in the sequence model, we employ two attention mechanisms to highlight important historical states of students. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which could more easily be applied to different applications. Finally, we conduct extensive experiments to evaluate the model on a large scale real-world dataset. The results prove the effectiveness of our model and the diversity of its application scenarios.