68,962 research outputs found
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Topic models, and more specifically the class of Latent Dirichlet Allocation
(LDA), are widely used for probabilistic modeling of text. MCMC sampling from
the posterior distribution is typically performed using a collapsed Gibbs
sampler. We propose a parallel sparse partially collapsed Gibbs sampler and
compare its speed and efficiency to state-of-the-art samplers for topic models
on five well-known text corpora of differing sizes and properties. In
particular, we propose and compare two different strategies for sampling the
parameter block with latent topic indicators. The experiments show that the
increase in statistical inefficiency from only partial collapsing is smaller
than commonly assumed, and can be more than compensated by the speedup from
parallelization and sparsity on larger corpora. We also prove that the
partially collapsed samplers scale well with the size of the corpus. The
proposed algorithm is fast, efficient, exact, and can be used in more modeling
situations than the ordinary collapsed sampler.Comment: Accepted for publication in Journal of Computational and Graphical
Statistic
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental problem
central to numerous applications of machine learning and statistics. This work
presents a principled approach for estimating broad classes of such models,
including probabilistic topic models and latent linear Bayesian networks, using
only second-order observed moments. The sufficient conditions for
identifiability of these models are primarily based on weak expansion
constraints on the topic-word matrix, for topic models, and on the directed
acyclic graph, for Bayesian networks. Because no assumptions are made on the
distribution among the latent variables, the approach can handle arbitrary
correlations among the topics or latent factors. In addition, a tractable
learning method via optimization is proposed and studied in numerical
experiments.Comment: 38 pages, 6 figures, 2 tables, applications in topic models and
Bayesian networks are studied. Simulation section is adde
Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data
We present a Bayesian non-negative tensor factorization model for
count-valued tensor data, and develop scalable inference algorithms (both batch
and online) for dealing with massive tensors. Our generative model can handle
overdispersed counts as well as infer the rank of the decomposition. Moreover,
leveraging a reparameterization of the Poisson distribution as a multinomial
facilitates conjugacy in the model and enables simple and efficient Gibbs
sampling and variational Bayes (VB) inference updates, with a computational
cost that only depends on the number of nonzeros in the tensor. The model also
provides a nice interpretability for the factors; in our model, each factor
corresponds to a "topic". We develop a set of online inference algorithms that
allow further scaling up the model to massive tensors, for which batch
inference methods may be infeasible. We apply our framework on diverse
real-world applications, such as \emph{multiway} topic modeling on a scientific
publications database, analyzing a political science data set, and analyzing a
massive household transactions data set.Comment: ECML PKDD 201
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
User information needs vary significantly across different tasks, and
therefore their queries will also differ considerably in their expressiveness
and semantics. Many studies have been proposed to model such query diversity by
obtaining query types and building query-dependent ranking models. These
studies typically require either a labeled query dataset or clicks from
multiple users aggregated over the same document. These techniques, however,
are not applicable when manual query labeling is not viable, and aggregated
clicks are unavailable due to the private nature of the document collection,
e.g., in email search scenarios. In this paper, we study how to obtain query
type in an unsupervised fashion and how to incorporate this information into
query-dependent ranking models. We first develop a hierarchical clustering
algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine
query types. Then, we study three query-dependent ranking models, including two
neural models that leverage query type information as additional features, and
one novel multi-task neural model that views query type as the label for the
auxiliary query cluster prediction task. This multi-task model is trained to
simultaneously rank documents and predict query types. Our experiments on tens
of millions of real-world email search queries demonstrate that the proposed
multi-task model can significantly outperform the baseline neural ranking
models, which either do not incorporate query type information or just simply
feed query type as an additional feature.Comment: CIKM 201
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