This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner. In this paper, we tackle the problem of information heterogeneity arising in multi-agent networks where the placement of informative agents plays a crucial role in the learning dynamics. Specifically, we propose BayGo, a novel fully decentralized joint Bayesian learning and graph optimization framework with proven fast convergence over a sparse graph. Under our framework, agents are able to learn and communicate with the most informative agent to their own learning. Unlike prior works, our framework assumes no prior knowledge of the data distribution across agents nor does it assume any knowledge of the true parameter of the system. The proposed alternating minimization based framework ensures global connectivity in a fully decentralized way while minimizing the number of communication links. We theoretically show that by optimizing the proposed objective function, the estimation error of the posterior probability distribution decreases exponentially at each iteration. Via extensive simulations, we show that our framework achieves faster convergence and higher accuracy compared to fully-connected and star topology graphs.

The framework of differential privacy (DP) upper bounds the information disclosure risk involved in using sensitive datasets for statistical analysis. A DP mechanism typically operates by adding carefully calibrated noise to the data release procedure. Generalized linear models (GLMs) are among the most widely used arms in data analyst's repertoire. In this work, with logistic and Poisson regression as running examples, we propose a generic noise-aware Bayesian framework to quantify the parameter uncertainty for a GLM at hand, given noisy sufficient statistics. We perform a tight privacy analysis and experimentally demonstrate that the posteriors obtained from our model, while adhering to strong privacy guarantees, are similar to the non-private posteriors.

We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches, which we assess for predictive performance and important medical AI properties such as interpretability, uncertainty reasoning, data-efficiency, and leveraging domain knowledge. Our Bayesian approach combines the flexibility of Gaussian processes with the structural power of neural networks to model biomarker progressions, without needing clinical labels for training. We run evaluations on the problem of Alzheimer's disease prediction, yielding results surpassing deep learning and with the practical advantages of Bayesian non-parametrics and probabilistic programming.

A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approach uses Clopper-Pearson confidence intervals for the Binomial distribution in conjunction with the histogram binning approach to calibrated prediction. In addition, we demonstrate how our predicted confidences can be used to enable downstream guarantees in two settings: (i) fast DNN inference, where we demonstrate how to compose a fast but inaccurate DNN with an accurate but slow DNN in a rigorous way to improve performance without sacrificing accuracy, and (ii) safe planning, where we guarantee safety when using a DNN to predict whether a given action is safe based on visual observations. In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs. Due to the recent success of machine learning, there has been increasing interest in using predictive models such as deep neural networks (DNNs) in safety-critical settings, such as robotics (e.g., obstacle detection (Ren et al., 2015) and forecasting (Kitani et al., 2012)) and healthcare (e.g., diagnosis (Gulshan et al., 2016; Esteva et al., 2017) and patient care management (Liao et al., 2020)).

Causal variables in Markov boundary (MB) have been widely applied in extensive single-label tasks. While few researches focus on the causal variable discovery in multi-label data due to the complex causal relationships. Since some variables in multi-label scenario might contain causal information about multiple labels, this paper investigates the problem of multi-label causal variable discovery as well as the distinguishing between common causal variables shared by multiple labels and label-specific causal variables associated with some single labels. Considering the multiple MBs under the non-positive joint probability distribution, we explore the relationships between common causal variables and equivalent information phenomenon, and find that the solutions are influenced by equivalent information following different mechanisms with or without existence of label causality. Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables. Similar to single-label problem, causal variables for multiple labels also have extensive application prospects. To demonstrate this, we apply the proposed causal mechanism to multi-label feature selection and present an interpretable algorithm, which is proved to achieve the minimal redundancy and the maximum relevance. Extensive experiments demonstrate the efficacy of these contributions.

When we think of machine learning, the first step is to acquire and train a large dataset. This problem is faced by hundreds of developers, especially for projects which have no previous developments. Certain GAN (Generative Adversarial Network) models, specifically Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN), have been introduced to produce realistic real-valued multi-dimensional time-series data. This paper brings the solution to this problem via the introduction of tsBNgen, a Python library to generate time series and sequential data based on an arbitrary dynamic Bayesian network. This package lets the developers and researchers generate time series data according to the random model they want.

The widespread interest in autonomous driving technology in recent years [2] has motivated extensive research in multiagent navigation in driving domains. One of the most challenging driving domains [3] is the uncontrolled intersection, i.e., a street intersection that features no traffic signs or signals. Within this domain, we focus on scenarios in which agents do not communicate explicitly or implicitly through e.g., turn signals. This model setup gives rise to challenging multi-vehicle encounters that mimic real-world situations (arising due to human distraction, violation of traffic rules or special emergencies) that result in fatal accidents [3]. The frequency and severity of such situations has motivated vivid research interest in uncontrolled intersections [4, 5, 6]. In the absence of explicit traffic signs, signals, rules or explicit communication among agents, avoiding collisions at intersections relies on the ability of agents to predict the dynamics of interaction amongst themselves. One prevalent way to model multiagent dynamics is via trajectory prediction. However, multistep multiagent trajectory prediction is NPhard [7], whereas the sample complexity of existing learning algorithms effectively prohibits the extraction of practical models. Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions with each other (rationality) compress the space of possible multiagent trajectories, effectively simplifying inference.

For cutting-edge AI researchers looking for clean semantics models to represent the context-specific causal dependencies essential for causal induction, this DeepMind's algorithm encourages you to look at good old-fashioned probability trees. The probability tree diagram is used to represent a probability space. Tree diagrams illustrate a series of independent events or conditional probabilities. The Node on the probability tree diagram represents an event, and it's probability. The root node represents a particular event where probability equals one.

In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network. This means that predictions by neural networks have biases which cannot be trivially distinguished from being due to the true nature of the creation and observation of data or not. In order to attempt to address such issues we discuss Bayesian neural networks: neural networks where the uncertainty due to the network can be characterised. In particular, we present the Bayesian statistical framework which allows us to categorise uncertainty in terms of the ingrained randomness of observing certain data and the uncertainty from our lack of knowledge about how data can be created and observed. In presenting such techniques we show how errors in prediction by neural networks can be obtained in principle, and provide the two favoured methods for characterising these errors. We will also describe how both of these methods have substantial pitfalls when put into practice, highlighting the need for other statistical techniques to truly be able to do inference when using neural networks.

With recent advancements in language technologies, humansare now interacting with technology through speech. To in-crease the reach of these technologies, we need to build suchsystems in local languages. A major bottleneck here are theunderlying data-intensive parts that make up such systems,including automatic speech recognition (ASR) systems thatrequire large amounts of labelled data. With the aim of aidingdevelopment of dialog systems in low resourced languages,we propose a novel acoustics based intent recognition systemthat uses discovered phonetic units for intent classification.The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio, and the second block which performs intent classifi-cation from the generated phonemic transcripts. Our workpresents results for such a system for two languages families- Indic languages and Romance languages, for two differentintent recognition tasks. We also perform multilingual train-ing of our intent classifier and show improved cross-lingualtransfer and performance on an unknown language with zeroresources in the same language family.