-- Compute and memory constraints have historically prevented traffic simulation software users from fully utilizing the predictive models underlying them. When calibrating car-following models, particularly, accommodations have included 1) using sensitivity analysis to limit the number of parameters to be calibrated, and 2) identifying only one set of parameter values using data collected from multiple car-following instances across multiple drivers. Shortcuts are further motivated by insufficient data set sizes, for which a driver may have too few instances to fully account for the variation in their driving behavior . In this paper, we demonstrate that recent technological advances can enable transportation researchers and engineers to overcome these constraints and produce calibration results that 1) outperform industry standard approaches, and 2) allow for a unique set of parameters to be estimated for each driver in a data set, even given a small amount of data. We propose a novel calibration procedure for car-following models based on Bayesian machine learning and probabilistic programming, and apply it to real-world data from a naturalistic driving study. We also discuss how this combination of mathematical and software tools can offer additional benefits such as more informative model validation and the incorporation of true-to-data uncertainty into simulation traces. Traffic simulation software packages are widely used in transportation engineering to estimate the impacts of potential changes to a roadway network and forecast system performance under future scenarios. These packages are underpinned by math-and physics-based models, which are designed to describe behavior at an aggregate (macroscopic) level or at the level of individual drivers (microscopic).

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain Ran Wang 1,2, Suhe Ye 1,2, Ke Li 3 and Sam Kwong 4 1 College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China. 2 Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen 518060, China. Abstract: Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible for predicting the relevance of one label. When training the classifier for a label, proceeding labels will be taken as extended features. If the extended features are highly correlated to the label, the performance will be improved, otherwise, the performance will not be influenced or even degraded. How to discover label correlation and determine the label order is critical for CC approach. This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC). First, conditional entropy is used to describe the dependency relations among labels. Then, a BN is built up by taking nodes as labels and weights of edges as their dependency relations. A new scoring function is proposed to evaluate a BN structure, and a heuristic algorithm is introduced to optimize the BN. At last, by applying topological sorting on the nodes of the optimized BN, the label order for constructing CC model is derived. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed method.

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change. In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. Our approach is motivated by approximating the complete data posterior of the model parameters. While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale. We derive interpretable closed-form solutions akin to existing active learning procedures for linear models, and generalize to arbitrary models using random projections. We demonstrate the benefits of our approach on several large-scale regression and classification tasks.

Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and decision making triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of these efforts, inference and control, as well as inference and belief space planning (BSP) are still treated as two separate processes. In this paper we propose a paradigm shift, a novel approach which deviates from conventional Bayesian inference and utilizes the similarities between inference and BSP. We make the key observation that inference can be efficiently updated using predictions made during the decision making stage, even in light of inconsistent data association between the two. We developed a two staged process that implements our novel approach and updates inference using calculations from the precursory planning phase. Using autonomous navigation in an unknown environment along with iSAM2 efficient methodologies as a test case, we benchmarked our novel approach against standard Bayesian inference, both with synthetic and real-world data (KITTI dataset). Results indicate that not only our approach improves running time by at least a factor of two while providing the same estimation accuracy, but it also alleviates the computational burden of state dimensionality and loop closures.

In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present Dueling Posterior Sampling (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the user's preferences. Because preference feedback is provided on trajectories rather than individual state/action pairs, we develop a Bayesian approach to solving the credit assignment problem, translating user preferences to a posterior distribution over state/action reward models. We prove an asymptotic no-regret rate for DPS with a Bayesian logistic regression credit assignment model; to our knowledge, this is the first regret guarantee for preference-based RL. We also discuss possible avenues for extending this proof methodology to analyze other credit assignment models. Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.

Suppose we scan 1 million similar women, and we tell everyone who tests positive that they have cancer. Then we will have correctly told all 10,000 women with cancer that they have it. Of the remaining 990,000 women whose lumps were benign, we will incorrectly tell 49,500 women that they have cancer. Therefore, of the women we identify as having cancer, about 83 percent will have been incorrectly diagnosed. Imagine you or a loved one received a positive test result.

Abstract: In this study, an efficient stochastic gradient - free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemb le randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework. The ENN is also robust to small training data size because the ensemble of stochastic realizations essentially enlarges the training dataset. This constitutes a desirable characteristic, especially for real - world engineering applications. In addition, the ENN does not require the c alculation of gradients, which enables the use of complicated neuron models and loss functions in neural networks. We experimentally demonstrate benefits of the proposed model, in particular showing that the ENN performs much better than the traditional Ba yesian neural networks (BNN). The EnRML in ENN is a substitution of gradient - based optimization algorithms, which means that it can be directly combined with the feed - forward process in other existing (deep) neural networks, such as convolutional neural ne tworks (CNN) and recurrent neural networks (RNN), broadening future applications of the ENN. Keywords: Inverse modeling, Gradient - free, Uncertainty quantification, Robust to small d ata size, Stochastic method 1. Introduction Artificial neural networks (ANN) are computing systems inspired by biological neural networks that constitute animal brains. ANN is capable of approximating nonlinear functional relationships between input and output variables (Kim et al., 2018). From a ma thematical perspective, a neural network can model any function up to any given precision with a sufficiently large number of basis functions (Cybenko, 1989; Hornik, 1991). In addition, we can even use much smaller models by constructing hierarchy neural n etworks (Delalleau & Bengio, 2011; Gal, 2016). The basic processing elements of neural networks are neurons. A collection of neurons is referred to as a layer, and the collection of interconnected layers forms the neural networks (Kim et al., 2018). A four - layer neural network is illustrated in Figure 1 as an example. In a neuron, the output is calculated by a nonlinear function of the sum of its inputs. The connections between different neurons from adjacent layers are represented by the weights in a model. The weights adjust as learning proceeds, and they represent the strength of the signal at a connection. The nonlinear function is also called the activation function, and the most popular choices are sigmoid, tansig, and ReLU (Li et al., 2015). 2 ANN has bee n widely applied to solving real - world engineering problems, and the following three topics are significant for effective applications .

Clustering time series is a delicate task; varying lengths and temporal offsets obscure direct comparisons. A natural strategy is to learn a parametric model foreach time series and to cluster the model parameters rather than the sequences themselves. Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical systems is a venerable task, permitting provably efficient solutions only in special cases. In this work, we show that clustering the parameters of unknown linear dynamical systems is, in fact, easier than identifying them. We analyze a computationally efficient clustering algorithm that enjoys provable convergence guarantees under a natural separation assumption. Although easy to implement, our algorithm is general, handling multi-dimensional data with time offsets and partial sequences. Evaluating our algorithm on both synthetic data and real electrocardiogram (ECG) signals, we see significant improvements in clustering quality over existing baselines.

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial distribution parameterized on the joint probability of each event, built following a hierarchy combining priors. Such a model allows us to incorporate extended domain expertise and article characteristics as prior knowledge, which in turn makes it possible for the underlying parameters to emerge thanks to sufficient data. Experiments are presented on real (anonymized) data from millions of customers along with a detailed discussion on the efficiency of such an approach within a large scale production system.

ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary components that have generated the data. The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding. The implemented algorithms are especially well-suited in cases when data consist of components that combine non-linearly and/or for data requiring flexible prior distributions. Furthermore, the implemented algorithms go beyond standard approaches by inferring prior and noise parameters of the data, and they provide rich a-posteriori approximations for inference. The library is designed to be extendable and it currently includes: Binary Sparse Coding (BSC), Ternary Sparse Coding (TSC), Discrete Sparse Coding (DSC), Maximal Causes Analysis (MCA), Maximum Magnitude Causes Analysis (MMCA), and Gaussian Sparse Coding (GSC, a recent spike-and-slab sparse coding approach). The algorithms are scalable due to a combination of variational approximations and parallelization. Implementations of all algorithms allow for parallel execution on multiple CPUs and multiple machines for medium to large-scale applications. Typical large-scale runs of the algorithms can use hundreds of CPUs to learn hundreds of dictionary elements from data with tens of millions of floating-point numbers such that models with several hundred thousand parameters can be optimized. The library is designed to have minimal dependencies and to be easy to use. It targets users of dictionary learning algorithms and Machine Learning researchers.