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Found 6 papers, 3 papers with code
The Power of Subsampling in Submodular Maximization
We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings.
How Do You Want Your Greedy: Simultaneous or Repeated?
We also present SubmodularGreedy. jl, a Julia package which implements these algorithms and may be downloaded at https://github. com/crharshaw/SubmodularGreedy. jl .
Balancing Covariates in Randomized Experiments with the Gram-Schmidt Walk Design
Asymptotically, the design perfectly balances all linear functions of a growing number of covariates with a diminishing reduction in robustness, effectively allowing experimenters to escape the compromise between balance and robustness in large samples.
Methodology Data Structures and Algorithms Statistics Theory Statistics Theory
Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
It is generally believed that submodular functions -- and the more general class of $\gamma$-weakly submodular functions -- may only be optimized under the non-negativity assumption $f(S) \geq 0$.
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
We also propose One-Shot Frank-Wolfe, a simpler algorithm which requires only a single stochastic gradient estimate in each round and achieves an $O(T^{2/3})$ stochastic regret bound for convex and continuous submodular optimization.
Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization
Sample Greedy achieves $(k + 3)$-approximation with only $O(nr/k)$ function evaluations.
