Search Results for author: Edoardo M. Airoldi

Found 17 papers, 3 papers with code

Stacking Models for Nearly Optimal Link Prediction in Complex Networks

2 code implementations17 Sep 2019 Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, Edoardo M. Airoldi, Aaron Clauset

These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions.

Link Prediction Meta-Learning

The Proximal Robbins-Monro Method

no code implementations4 Oct 2015 Panos Toulis, Thibaut Horel, Edoardo M. Airoldi

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 Optimization

Stochastic gradient descent methods for estimation with large data sets

1 code implementation22 Sep 2015 Dustin Tran, Panos Toulis, Edoardo M. Airoldi

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

no code implementations3 Jul 2015 Guillaume W. Basse, Edoardo M. Airoldi

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

no code implementations25 Jun 2015 Daniel L. Sussman, Alexander Volfovsky, Edoardo M. Airoldi

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

no code implementations NeurIPS 2015 Dustin Tran, David M. Blei, Edoardo M. Airoldi

We develop a general variational inference method that preserves dependency among the latent variables.

Stochastic Optimization Variational Inference

Towards stability and optimality in stochastic gradient descent

no code implementations10 May 2015 Panos Toulis, Dustin Tran, Edoardo M. Airoldi

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

no code implementations21 Dec 2014 Aviv Tamar, Panos Toulis, Shie Mannor, Edoardo M. Airoldi

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

no code implementations31 Oct 2014 Qiuyi Han, Kevin S. Xu, Edoardo M. Airoldi

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

no code implementations13 Aug 2014 Panos Toulis, Edoardo M. Airoldi

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

no code implementations11 May 2014 Elham Azizi, James E. Galagan, Edoardo M. Airoldi

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

1 code implementation NeurIPS 2013 Edoardo M. Airoldi, Thiago B. Costa, Stanley H. Chan

Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.

Stochastic Block Model

A Poisson convolution model for characterizing topical content with word frequency and exclusivity

no code implementations18 Jun 2012 Edoardo M. Airoldi, Jonathan M Bischof

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

no code implementations4 Dec 2010 Edoardo M. Airoldi, Thiago Costa, Federico Bassetti, Fabrizio Leisen, Michele Guindani

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

no code implementations29 Dec 2009 Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi

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

no code implementations28 Dec 2009 Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?

Information Retrieval Relational Reasoning +1

Geometric Representations of Random Hypergraphs

no code implementations18 Dec 2009 Simón Lunagómez, Sayan Mukherjee, Robert L. Wolpert, Edoardo M. Airoldi

A parametrization of hypergraphs based on the geometry of points in $\mathbf{R}^d$ is developed.

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