
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Most realworld networks are incompletely observed. Algorithms that can ...
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Sampling on Social Networks from a Decision Theory Perspective
Some of the most used sampling mechanisms that propagate through a socia...
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A systematic investigation of classical causal inference strategies under misspecification due to network interference
We systematically investigate issues due to misspecification that arise...
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Optimizing clusterbased randomized experiments under a monotonicity assumption
Clusterbased randomized experiments are popular designs for mitigating ...
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Propensity score methodology in the presence of network entanglement between treatments
In experimental design and causal inference, it may happen that the trea...
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Implicit stochastic approximation
The need to carry out parameter estimation from massive data has reinvig...
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Stochastic gradient descent methods for estimation with large data sets
We develop methods for parameter estimation in settings with largescale...
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Modelassisted design of experiments in the presence of network correlated outcomes
We consider the problem of how to assign treatment in a randomized exper...
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Analyzing statistical and computational tradeoffs of estimation procedures
The recent explosion in the amount and dimensionality of data has exacer...
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Copula variational inference
We develop a general variational inference method that preserves depende...
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Towards stability and optimality in stochastic gradient descent
Iterative procedures for parameter estimation based on stochastic gradie...
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Implicit Temporal Differences
In reinforcement learning, the TD(λ) algorithm is a fundamental policy e...
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Learning modular structures from network data and node variables
A standard technique for understanding underlying dependency structures ...
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Stochastic blockmodels with growing number of classes
We present asymptotic and finitesample results on the use of stochastic...
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A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for dis...
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Geometric Representations of Random Hypergraphs
A parametrization of hypergraphs based on the geometry of points in R^d ...
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Getting started in probabilistic graphical models
Probabilistic graphical models (PGMs) have become a popular tool for com...
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Mixed membership stochastic blockmodels
Observations consisting of measurements on relationships for pairs of ob...
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Edoardo M. Airoldi
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Associate Professor of Statistics at Harvard University