Causal discovery without intervention is well recognized as a challenging yet powerful data analysis tool, boosting the development of other scientific areas, such as biology, astronomy, and social science. The major technical difficulty behind the observation-based causal discovery is to effectively and efficiently identify causes and effects from correlated variables given the existence of significant noises. Previous studies mostly employ two very different methodologies under Bayesian network framework, namely global likelihood maximization and locally complexity analysis over marginal distributions. While these approaches are effective in their respective problem domains, in this paper, we show that they can be combined to formulate a new global optimization model with local statistical significance, called structural equational likelihood framework (or SELF in short). We provide thorough analysis on the soundness of the model under mild conditions and present efficient heuristic-based algorithms for scalable model training. Empirical evaluations using XGBoost validate the superiority of our proposal over state-of-the-art solutions, on both synthetic and real world causal structures.

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.

Distributions over rankings are used to model user preferences in various settings including political elections and electronic commerce. The Repeated Insertion Model (RIM) gives rise to various known probability distributions over rankings, in particular to the popular Mallows model. However, probabilistic inference on RIM is computationally challenging, and provably intractable in the general case. In this paper we propose an algorithm for computing the marginal probability of an arbitrary partially ordered set over RIM. We analyze the complexity of the algorithm in terms of properties of the model and the partial order, captured by a novel measure termed the "cover width." We also conduct an experimental study of the algorithm over serial and parallelized implementations. Building upon the relationship between inference with rank distributions and counting linear extensions, we investigate the inference problem when restricted to partial orders that lend themselves to efficient counting of their linear extensions.

Inspired by the Bayesian brain hypothesis and deep learning, we develop a Bayesian autoencoder, a method of constructing recognition systems using a Bayesian network. We construct hierarchical Bayesian networks based on feature extraction and implement pooling to achieve invariance within a Bayesian network framework. The constructed networks propagate information bidirectionally between layers. We expect they will be able to achieve brain-like recognition using local features and global information such as their environments.

We tackle the problem of authenticating high value Italian wines through machine learning classification. The problem is a seriuos one, since protection of high quality wines from forgeries is worth several million of Euros each year. In a previous work we have identified some base models (in particular classifiers based on Bayesian network (BNC), multi-layer perceptron (MLP) and sequential minimal optimization (SMO)) that well behave using unexpensive chemical analyses of the interested wines. In the present paper, we investigate the role of esemble learning in the construction of more robust classifiers; results suggest that, while bagging and boosting may significantly improve both BNC and MLP, the SMO model is already very robust and efficient as a base learner. We report on results concerning both cross validation on two different datasets, as well as experiments with models trained with the above datasets and tested with a dataset of potentially fake wines; this has been synthesized from a generative probabilistic model learned from real samples and expert knowledge.

How to robustly and accurately extract articulated skeletons from point set sequences captured by a single consumer-grade depth camera still remains to be an unresolved challenge to date. To address this issue, we propose a novel, unsupervised approach consisting of three contributions (steps): (i) a non-rigid point set registration algorithm to first build one-to-one point correspondences among the frames of a sequence; (ii) a skeletal structure extraction algorithm to generate a skeleton with reasonable numbers of joints and bones; (iii) a skeleton joints estimation algorithm to achieve accurate joints. At the end, our method can produce a quality articulated skeleton from a single 3D point sequence corrupted with noise and outliers. The experimental results show that our approach soundly outperforms state of the art techniques, in terms of both visual quality and accuracy.

Person re-identification (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufficient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a re-identification probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.

Heterogeneous face recognition (HFR) refers to matching a probe face image taken from one modality to face images acquired from another modality. It plays an important role in security scenarios. However, HFR is still a challenging problem due to great discrepancies between cross-modality images. This paper proposes an asymmetric joint learning (AJL) approach to handle this issue. The proposed method transforms the cross-modality differences mutually by incorporating the synthesized images into the learning process which provides more discriminative information. Although the aggregated data would augment the scale of intra-classes, it also reduces the diversity (i.e. discriminative information) for inter-classes. Then, we develop the AJL model to balance this dilemma. Finally, we could obtain the similarity score between two heterogeneous face images through the log-likelihood ratio. Extensive experiments on viewed sketch database, forensic sketch database and near infrared image database illustrate that the proposed AJL-HFR method achieve superior performance in comparison to state-of-the-art methods.

Probabilistic topic models are popular unsupervised learning methods, including probabilistic latent semantic indexing (pLSI) and latent Dirichlet allocation (LDA). By now, their training is implemented on general purpose computers (GPCs), which are flexible in programming but energy-consuming. Towards low-energy implementations, this paper investigates their training on an emerging hardware technology called the neuromorphic multi-chip systems (NMSs). NMSs are very effective for a family of algorithms called spiking neural networks (SNNs). We present three SNNs to train topic models.The first SNN is a batch algorithm combining the conventional collapsed Gibbs sampling (CGS) algorithm and an inference SNN to train LDA. The other two SNNs are online algorithms targeting at both energy- and storage-limited environments. The two online algorithms are equivalent with training LDA by using maximum-a-posterior estimation and maximizing the semi-collapsed likelihood, respectively.They use novel, tailored ordinary differential equations for stochastic optimization. We simulate the new algorithms and show that they are comparable with the GPC algorithms, while being suitable for NMS implementation. We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.

Probabilistic Sentential Decision Diagrams (PSDDs) have been proposed for learning tractable probability distributions from a combination of data and background knowledge (in the form of Boolean constraints). In this paper, we propose a variant on PSDDs, called conditional PSDDs, for representing a family of distributions that are conditioned on the same set of variables. Conditional PSDDs can also be learned from a combination of data and (modular) background knowledge. We use conditional PSDDs to define a more structured version of Bayesian networks, in which nodes can have an exponential number of states, hence expanding the scope of domains where Bayesian networks can be applied. Compared to classical PSDDs, the new representation exploits the independencies captured by a Bayesian network to decompose the learning process into localized learning tasks, which enables the learning of better models while using less computation. We illustrate the promise of conditional PSDDs and structured Bayesian networks empirically, and by providing a case study to the modeling of distributions over routes on a map.