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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.
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.
Maximization of Approximately Submodular Functions
We study the problem of maximizing a function that is approximately submodular under a cardinality constraint.
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.
