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05/25/2022
Graph Agnostic Randomized Experimental Design
Randomized experiments are widely used to estimate causal effects of pro...
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03/18/2022
Optimizing Randomized and Deterministic Saturation Designs under Interference
Randomized saturation designs are a family of designs which assign a pos...
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09/17/2019
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Most real-world networks are incompletely observed. Algorithms that can ...
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11/19/2018
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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10/18/2018
A systematic investigation of classical causal inference strategies under mis-specification due to network interference
We systematically investigate issues due to mis-specification that arise...
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03/07/2018
Optimizing cluster-based randomized experiments under a monotonicity assumption
Cluster-based randomized experiments are popular designs for mitigating ...
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01/22/2018
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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10/04/2015
Implicit stochastic approximation
The need to carry out parameter estimation from massive data has reinvig...
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09/22/2015
Stochastic gradient descent methods for estimation with large data sets
We develop methods for parameter estimation in settings with large-scale...
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07/03/2015
Model-assisted 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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06/25/2015
Analyzing statistical and computational tradeoffs of estimation procedures
The recent explosion in the amount and dimensionality of data has exacer...
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06/10/2015
Copula variational inference
We develop a general variational inference method that preserves depende...
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05/10/2015
Towards stability and optimality in stochastic gradient descent
Iterative procedures for parameter estimation based on stochastic gradie...
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12/21/2014
Implicit Temporal Differences
In reinforcement learning, the TD(λ) algorithm is a fundamental policy e...
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05/11/2014
Learning modular structures from network data and node variables
A standard technique for understanding underlying dependency structures ...
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11/21/2010
Stochastic blockmodels with growing number of classes
We present asymptotic and finite-sample results on the use of stochastic...
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12/29/2009
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for dis...
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12/18/2009
Geometric Representations of Random Hypergraphs
A parametrization of hypergraphs based on the geometry of points in R^d ...
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06/14/2007
Getting started in probabilistic graphical models
Probabilistic graphical models (PGMs) have become a popular tool for com...
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05/30/2007
