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Optimal Bounds between f-Divergences and Integral Probability Metrics
The families of f-divergences (e. g. the Kullback-Leibler divergence) and Integral Probability Metrics (e. g. total variation distance or maximum mean discrepancies) are commonly used in optimization and estimation.
Estimation of Models with Limited Data by Leveraging Shared Structure
Modern data sets, such as those in healthcare and e-commerce, are often derived from many individuals or systems but have insufficient data from each source alone to separately estimate individual, often high-dimensional, model parameters.
Optimal Bounds between $f$-Divergences and Integral Probability Metrics
The families of $f$-divergences (e. g. the Kullback-Leibler divergence) and Integral Probability Metrics (e. g. total variation distance or maximum mean discrepancies) are widely used to quantify the similarity between probability distributions.
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.
Inferring Graphs from Cascades: A Sparse Recovery Framework
In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph.
