A General Framework for Uncertainty Estimation in Deep Learning

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy.Comment: Accepted for publication in the Robotics and Automation Letters 2020, and for presentation at the International Conference on Robotics and Automation (ICRA) 202

Approximate Bayesian Deep Learning for Resource-Constrained Environments

Deep learning models have shown promising results in areas including computer vision, natural language processing, speech recognition, and more. However, existing point estimation-based training methods for these models may result in predictive uncertainties that are not well calibrated, including the occurrence of confident errors. Approximate Bayesian inference methods can help address these issues in a principled way by accounting for uncertainty in model parameters. However, these methods are computationally expensive both when computing approximations to the parameter posterior and when using an approximate parameter posterior to make predictions. They can also require significantly more storage than point-estimated models. In this thesis, we address a range of questions related to trade-offs between the quality of inference and prediction and the computational scalability of Bayesian deep learning methods. We begin by developing a framework for comprehensive evaluation of Bayesian neural network models and applying this framework to a range of existing models and inference methods. Second, we address the problem of providing flexible trade-offs between prediction quality, run time, and storage by developing and evaluating a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier. Third, we investigate the trade-offs between model sparsity and inference performance for deep neural network models using several approaches to deriving sparse model structures. Fourth, we present a framework for correcting approximate posterior predictive distributions, encouraging them to prefer high-utility decisions. Finally, we investigate the use of approximate Bayesian deep learning in object detection and present an evaluation of approaches for quantifying different facets of uncertainty related to object classes and locations

Controlling Risk of Web Question Answering

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva

Representation learning for uncertainty-aware clinical decision support

Over the last decade, there has been an increasing trend towards digitalization in healthcare, where a growing amount of patient data is collected and stored electronically. These recorded data are known as electronic health records. They are the basis for state-of-the-art research on clinical decision support so that better patient care can be delivered with the help of advanced analytical techniques like machine learning. Among various technical fields in machine learning, representation learning is about learning good representations from raw data to extract useful information for downstream prediction tasks. Deep learning, a crucial class of methods in representation learning, has achieved great success in many fields such as computer vision and natural language processing. These technical breakthroughs would presumably further advance the research and development of data analytics in healthcare. This thesis addresses clinically relevant research questions by developing algorithms based on state-of-the-art representation learning techniques. When a patient visits the hospital, a physician will suggest a treatment in a deterministic manner. Meanwhile, uncertainty comes into play when the past statistics of treatment decisions from various physicians are analyzed, as they would possibly suggest different treatments, depending on their training and experiences. The uncertainty in clinical decision-making processes is the focus of this thesis. The models developed for supporting these processes will therefore have a probabilistic nature. More specifically, the predictions are predictive distributions in regression tasks and probability distributions over, e.g., different treatment decisions, in classification tasks. The first part of the thesis is concerned with prescriptive analytics to provide treatment recommendations. Apart from patient information and treatment decisions, the outcome after the respective treatment is included in learning treatment suggestions. The problem setting is known as learning individualized treatment rules and is formulated as a contextual bandit problem. A general framework for learning individualized treatment rules using data from observational studies is presented based on state-of-the-art representation learning techniques. From various offline evaluation methods, it is shown that the treatment policy in our proposed framework can demonstrate better performance than both physicians and competitive baselines. Subsequently, the uncertainty-aware regression models in diagnostic and predictive analytics are studied. Uncertainty-aware deep kernel learning models are proposed, which allow the estimation of the predictive uncertainty by a pipeline of neural networks and a sparse Gaussian process. By considering the input data structure, respective models are developed for diagnostic medical image data and sequential electronic health records. Various pre-training methods from representation learning are adapted to investigate their impacts on the proposed models. Through extensive experiments, it is shown that the proposed models delivered better performance than common architectures in most cases. More importantly, uncertainty-awareness of the proposed models is illustrated by systematically expressing higher confidence in more accurate predictions and less confidence in less accurate ones. The last part of the thesis is about missing data imputation in descriptive analytics, which provides essential evidence for subsequent decision-making processes. Rather than traditional mean and median imputation, a more advanced solution based on generative adversarial networks is proposed. The presented method takes the categorical nature of patient features into consideration, which enables the stabilization of the adversarial training. It is shown that the proposed method can better improve the predictive accuracy compared to traditional imputation baselines

Reliable deep reinforcement learning: stable training and robust deployment

Deep reinforcement learning (RL) represents a data-driven framework for sequential decision making that has demonstrated the ability to solve challenging control tasks. This data-driven, learning-based approach offers the potential to improve operations in complex systems, but only if it can be trusted to produce reliable performance both during training and upon deployment. These requirements have hindered the adoption of deep RL in many real-world applications. In order to overcome the limitations of existing methods, this dissertation introduces reliable deep RL algorithms that deliver (i) stable training from limited data and (ii) robust, safe deployment in the presence of uncertainty. The first part of the dissertation addresses the interactive nature of deep RL, where learning requires data collection from the environment. This interactive process can be expensive, time-consuming, and dangerous in many real-world settings, which motivates the need for reliable and efficient learning. We develop deep RL algorithms that guarantee stable performance throughout training, while also directly considering data efficiency in their design. These algorithms are supported by novel policy improvement lower bounds that account for finite-sample estimation error and sample reuse. The second part of the dissertation focuses on the uncertainty present in real-world applications, which can impact the performance and safety of learned control policies. In order to reliably deploy deep RL in the presence of uncertainty, we introduce frameworks that incorporate safety constraints and provide robustness to general disturbances in the environment. Importantly, these frameworks make limited assumptions on the training process, and can be implemented in settings that require real-world interaction for training. This motivates deep RL algorithms that deliver robust, safe performance at deployment time, while only using standard data collection from a single training environment. Overall, this dissertation contributes new techniques to overcome key limitations of deep RL for real-world decision making and control. Experiments across a variety of continuous control tasks demonstrate the effectiveness of our algorithms

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable, we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allows for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art