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Found 17 papers, 3 papers with code
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions.
The Proximal Robbins-Monro Method
Exact implementations of the proximal Robbins-Monro procedure are challenging, but we show that approximate implementations lead to procedures that are easy to implement, and still dominate classical procedures by achieving numerical stability, practically without tradeoffs.
Stochastic gradient descent methods for estimation with large data sets
When the update is based on a noisy gradient, the stochastic approximation is known as standard stochastic gradient descent, which has been fundamental in modern applications with large data sets.
Model-assisted design of experiments in the presence of network correlated outcomes
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention.
Analyzing statistical and computational tradeoffs of estimation procedures
The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful.
Copula variational inference
We develop a general variational inference method that preserves dependency among the latent variables.
Towards stability and optimality in stochastic gradient descent
For statistical efficiency, AI-SGD employs averaging of the iterates, which achieves the optimal Cram\'{e}r-Rao bound under strong convexity, i. e., it is an optimal unbiased estimator of the true parameter value.
Implicit Temporal Differences
In reinforcement learning, the TD($\lambda$) algorithm is a fundamental policy evaluation method with an efficient online implementation that is suitable for large-scale problems.
Consistent estimation of dynamic and multi-layer block models
Significant progress has been made recently on theoretical analysis of estimators for the stochastic block model (SBM).
Methodology Social and Information Networks Statistics Theory Physics and Society Statistics Theory
Asymptotic and finite-sample properties of estimators based on stochastic gradients
Here, we introduce implicit stochastic gradient descent procedures, which involve parameter updates that are implicitly defined.
Learning modular structures from network data and node variables
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group.
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.
A Poisson convolution model for characterizing topical content with word frequency and exclusivity
We consider a setting where professional editors have annotated documents to a collection of topic categories, organized into a tree, in which leaf-nodes correspond to the most specific topics.
Generalized Species Sampling Priors with Latent Beta reinforcements
We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models.
A survey of statistical network models
Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation.
Ranking relations using analogies in biological and information networks
Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?
Geometric Representations of Random Hypergraphs
A parametrization of hypergraphs based on the geometry of points in $\mathbf{R}^d$ is developed.
