137 citations · 650 across the 48 of their papers we have counts for
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cs.DS2020
Robust Algorithms for Online Convex Problems via Primal-Dual
Marco Molinaro
Primal-dual methods in online optimization give several of the state-of-the art results in both of the most common models: adversarial and stochastic/random order. Here we try to p…
cs.DS2020
Knapsack Secretary with Bursty Adversary
Thomas Kesselheim, Marco Molinaro
The random-order or secretary model is one of the most popular beyond-worst case model for online algorithms. While it avoids the pessimism of the traditional adversarial model, in…
cs.DS2020★ 9 cited
Curvature of Feasible Sets in Offline and Online Optimization
Marco Molinaro
It is known that the curvature of the feasible set in convex optimization allows for algorithms with better convergence rates, and there has been renewed interest in this topic bot…