In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: We introduce a copula mixture model to perform dependency-seeking clustering when cooccurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary continuous marginal densities. We formulate our model as a non-parametric Bayesian mixture, while providing efficient MCMC inference. Experiments on synthetic and real data demonstrate that the increased flexibility of the copula mixture significantly improves the clustering and the interpretability of the results.
Example Output: Copula Mixture Model for Dependency-seeking Clustering

Example Input: Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.
Example Output: SINGLE CHANNEL SPEECH ENHANCEMENT USING OUTLIER DETECTION

Example Input: In this paper, we introduce a new notion of algorithmic stability called typical stability. When our goal is to release real-valued queries (statistics) computed over a dataset, this notion does not require the queries to be of bounded sensitivity – a condition that is generally assumed under a standard notion of algorithmic stability known as differential privacy [DMNS06, Dwo06]. Instead, typical stability requires the output of the query, when computed on a dataset drawn from the underlying distribution, to be “well-concentrated” around its expected value with respect to that distribution. Typical stability can also be motivated as an alternative definition for database privacy (in such case, we call it typical privacy). Like differential privacy, this notion enjoys several important properties including robustness to post-processing and adaptive composition. We also discuss the guarantees of typical stability on the generalization error (i.e., the difference between the value of the query computed on the dataset and the expected value of the query with respect to the true data distribution). We show that these guarantees hold for a broader class of queries than that of bounded-sensitivity queries. This class contains all queries whose output distributions have a “light” tail, e.g., subgaussian and subexponential queries. In particular, we show that if a typically stable interaction with a dataset yields a query from that class, then this query when evaluated on the same dataset will have small generalization error with high probability (i.e., it will not overfit to the dataset). We discuss the composition guarantees of typical stability and prove a composition theorem that gives a characterization of the degradation of the parameters of typical stability/privacy under k-fold adaptive composition. We also give simple noise-addition algorithms that achieve this notion. These algorithms are similar to their differentially private counterparts, however, the added noise is calibrated differently. ∗University of California San Diego, Center for Information Theory and Applications and Department of Computer Science and Engineering. rbassily@ucsd.edu †University of California San Diego, Department of Computer Science and Engineering. yfreund@eng.ucsd.edu ar X iv :1 60 4. 03 33 6v 1 [ cs .L G ] 1 2 A pr 2 01 6
Example Output:
Typicality-Based Stability and Privacy