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Deflation Methods for Sparse PCA
TLDR
This work develops several deflation alternatives better suited to the cardinality-constrained context and reformulates the sparse PCA optimization problem to explicitly reflect the maximum additional variance objective on each round, resulting in a generalized deflation procedure that typically outperforms more standard techniques on real-world datasets. Expand
Divide-and-Conquer Matrix Factorization
TLDR
The experiments with collaborative filtering, video background modeling, and simulated data demonstrate the near-linear to super-linear speed-ups attainable with DFC, and the analysis shows that DFC enjoys high-probability recovery guarantees comparable to those of its base algorithm. Expand
Matrix concentration inequalities via the method of exchangeable pairs
This paper derives exponential concentration inequalities and polynomial moment inequalities for the spectral norm of a random matrix. The analysis requires a matrix extension of the scalarExpand
Feature-Weighted Linear Stacking
TLDR
A linear technique, Feature-Weighted Linear Stacking (FWLS), that incorporates meta-features for improved accuracy while retaining the well-known virtues of linear regression regarding speed, stability, and interpretability is presented. Expand
Measuring Sample Quality with Stein's Method
TLDR
This work introduces a new computable quality measure based on Stein's method that quantifies the maximum discrepancy between sample and target expectations over a large class of test functions and uses this tool to compare exact, biased, and deterministic sample sequences. Expand
On the Consistency of Ranking Algorithms
TLDR
A new value-regularized linear loss is presented, establish its consistency under reasonable assumptions on noise, and show that it outperforms conventional ranking losses in a collaborative filtering experiment. Expand
Measuring Sample Quality with Kernels
TLDR
A theory of weak convergence for K SDs based on Stein's method is developed, it is demonstrated that commonly used KSDs fail to detect non-convergence even for Gaussian targets, and it is shown that kernels with slowly decaying tails provably determine convergence for a large class of target distributions. Expand
Corrupted Sensing: Novel Guarantees for Separating Structured Signals
TLDR
This work analyzes both penalized programs that tradeoff between signal and corruption complexity, and constrained programs that bound the complexity of signal or corruption when prior information is available, and provides new interpretable bounds for the Gaussian complexity of sparse vectors, block-sparse vectors, and low-rank matrices. Expand
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network
TLDR
The novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes, is made. Expand
Combinatorial Clustering and the Beta Negative Binomial Process
We develop a Bayesian nonparametric approach to a general family of latent class problems in which individuals can belong simultaneously to multiple classes and where each class can be exhibitedExpand
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