Scalable algorithms for latent variable models in machine learning

Latent variable modeling (LVM) is a popular approach in many machine learning applications, such as recommender systems and topic modeling, due to its ability to succinctly represent data, even in the presence of several missing entries. Existing learning methods for LVMs, while attractive, are infeasible for the large-scale datasets required in modern big data applications. In addition, such applications often come with various types of side information such as the text description of items and the social network among users in a recommender system. In this thesis, we present scalable learning algorithms for a wide range of latent variable models such as low-rank matrix factorization and latent Dirichlet allocation. We also develop simple but effective techniques to extend existing LVMs to exploit various types of side information and make better predictions in many machine learning applications such as recommender systems, multi-label learning, and high-dimensional time-series prediction. In addition, we also propose a novel approach for the maximum inner product search problem to accelerate the prediction phase of many latent variable models.Computer Science

CPCP violation in charmed hadron decays into neutral kaons

We find a new $CP$ violating effect in charmed hadron decays into neutral kaons, which is induced by the interference between the Cabibbo-favored and doubly Cabibbo-suppressed amplitudes with the $K^{0}-\overline K^{0}$ mixing. It is estimated to be of order of $\mathcal{O}(10^{-3})$, much larger than the direct $CP$ asymmetry, but missed in the literature. To reveal this new $CP$ violation effect, we propose a new observable, the difference of the $CP$ asymmetries in the $D^{+}\to \pi^{+}K_S^0$ and $D_{s}^{+}\to K^{+} K_S^0$ modes. Once the new effect is determined by experiments, the direct $CP$ asymmetry then can be extracted and used to search for new physics.Comment: 6 pages, 3 figures. Contribution to the proceeding of The 15th International Conference on Flavor Physics & CP Violation, 5-9 June 2017, Prague, Czech Republi

Implications on the first observation of charm CPV at LHCb

Very recently, the LHCb Collaboration observed the $CP$ violation (CPV) in the charm sector for the first time, with $\Delta A_{CP}^{\rm dir}\equiv A_{CP}(D^0\to K^+K^-)-A_{CP}(D^0\to \pi^+\pi^-)=(-1.54\pm0.29)\times10^{-3}$. This result is consistent with our prediction of $\Delta A_{CP}^{\rm SM}=(-0.57\sim -1.87)\times 10^{-3}$ obtained in the factorization-assisted topological-amplitude (FAT) approach in [PRD86,036012(2012)]. It implies that the current understanding of the penguin dynamics in charm decays in the Standard Model is reasonable. Motivated by the success of the FAT approach, we further suggest to measure the $D^+\to K^+K^-\pi^+$ decay, which is the next potential mode to reveal the CPV of the same order as $10^{-3}$.Comment: 10 page

Large-scale Multi-label Learning with Missing Labels

The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b) the ability to handle data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is surprisingly able to encompass several recent label-compression based methods which can be derived as special cases of our method. To optimize the ERM problem, we develop techniques that exploit the structure of specific loss functions - such as the squared loss function - to offer efficient algorithms. We further show that our learning framework admits formal excess risk bounds even in the presence of missing labels. Our risk bounds are tight and demonstrate better generalization performance for low-rank promoting trace-norm regularization when compared to (rank insensitive) Frobenius norm regularization. Finally, we present extensive empirical results on a variety of benchmark datasets and show that our methods perform significantly better than existing label compression based methods and can scale up to very large datasets such as the Wikipedia dataset