Giving people the power to build community is central to Facebook's mission. Technically, searching for communities poses very different challenges compared to the standard IR problems. First, there is a vocabulary mismatch problem since most of the content of the communities is private. Second, the common labeling strategies based on human ratings and clicks do not work well due to limited public content available to third-party raters and users at search time. Finally, community search has a dual objective of satisfying searchers and growing the number of active communities. While A/B testing is a well known approach for assessing the former, it is an open question on how to measure progress on the latter. This talk discusses these challenges in depth and describes our solution.

We propose a generic stochastic expectation-maximization (EM) algorithm for the estimation of high-dimensional latent variable models. At the core of our algorithm is a novel semi-stochastic variance-reduced gradient designed for the $Q$-function in the EM algorithm. Under a mild condition on the initialization, our algorithm is guaranteed to attain a linear convergence rate to the unknown parameter of the latent variable model, and achieve an optimal statistical rate up to a logarithmic factor for parameter estimation. Compared with existing high-dimensional EM algorithms, our algorithm enjoys a better computational complexity and is therefore more efficient. We apply our generic algorithm to two illustrative latent variable models: Gaussian mixture model and mixture of linear regression, and demonstrate the advantages of our algorithm by both theoretical analysis and numerical experiments. We believe that the proposed semi-stochastic gradient is of independent interest for general nonconvex optimization problems with bivariate structures.

In a collection of documents, such as news articles or tweets, various events take place over time. The event detection problem aims at discovering significant events that have not been mentioned before the detection time. When these events occur, we observe that topic distributions of documents will diverge notably. However, event detection from such divergence may be hampered by noises. In this paper, we propose TopicDiver, a novel method to address the event detection problem. TopicDiver models topic distributions of documents over time and filters out noises while capturing the useful divergence between such distributions. The direct exploitation of topic distribution over time sets our work apart from existing studies on event detection. We conduct comprehensive experiments under different settings on news and Twitter data. The experimental results demonstrate that TopicDiver outperforms the baseline models in the measures for accuracy across various settings.

In this paper, we propose a novel algorithm based on nonconvex sparsity-inducing penalty for one-bit compressed sensing. We prove that our algorithm has a sample complexity of O(s/ε^2) for strong signals, and O(s\log d/ε^2) for weak signals, where s is the number of nonzero entries in the signal vector, d is the signal dimension and εis the recovery error. For general signals, the sample complexity of our algorithm lies between O(s/ε^2) and O(s\log d/ε^2). This is a remarkable improvement over the existing best sample complexity O(s\log d/ε^2). Furthermore, we show that our algorithm achieves exact support recovery with high probability for strong signals. Our theory is verified by extensive numerical experiments, which clearly illustrate the superiority of our algorithm for both approximate signal and support recovery in the noisy setting.