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Found 14 papers, 1 papers with code
Energy-Efficient Scheduling with Predictions
We show that, when the prediction error is small, this framework gives improved competitive ratios for many different energy-efficient scheduling problems, including energy minimization with deadlines, while also maintaining a bounded competitive ratio regardless of the prediction error.
Scheduling with Speed Predictions
In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown.
Learning Low Degree Hypergraphs
To the best of our knowledge, these are the first algorithms with poly$(n, m)$ query complexity for learning non-trivial families of hypergraphs that have a super-constant number of edges of super-constant size.
Instance Specific Approximations for Submodular Maximization
A major question is therefore how to measure the performance of an algorithm in comparison to an optimal solution on instances we encounter in practice.
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
Both the upper and lower bounds are under the assumption that queries are only on feasible sets (i. e., of size at most k).
Adversarial Attacks on Binary Image Recognition Systems
Nevertheless, we generalize SCAR to design attacks that fool state-of-the-art check processing systems using unnoticeable perturbations that lead to misclassification of deposit amounts.
The FAST Algorithm for Submodular Maximization
Recent algorithms have comparable guarantees in terms of asymptotic worst case analysis, but their actual number of rounds and query complexity depend on very large constants and polynomials in terms of precision and confidence, making them impractical for large data sets.
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization
For the problem of minimizing a non-smooth convex function $f:[0, 1]^n\to \mathbb{R}$ over the unit Euclidean ball, we give a tight lower bound that shows that even when $\texttt{poly}(n)$ queries can be executed in parallel, there is no randomized algorithm with $\tilde{o}(n^{1/3})$ rounds of adaptivity that has convergence rate that is better than those achievable with a one-query-per-round algorithm.
Approximation Guarantees for Adaptive Sampling
In particular, we show that under very mild conditions of curvature of a function, adaptive sampling techniques achieve an approximation arbitrarily close to 1/2 while maintaining their low adaptivity.
Learning to Optimize Combinatorial Functions
Submodular functions have become a ubiquitous tool in machine learning.
Minimizing a Submodular Function from Samples
In this paper we consider the problem of minimizing a submodular function from training data.
Statistical Cost Sharing
We first show that when cost functions come from the family of submodular functions with bounded curvature, $\kappa$, the Shapley value can be approximated from samples up to a $\sqrt{1 - \kappa}$ factor, and that the bound is tight.
The Power of Optimization from Samples
In this paper we show that for any monotone submodular function with curvature c there is a (1 - c)/(1 + c - c^2) approximation algorithm for maximization under cardinality constraints when polynomially-many samples are drawn from the uniform distribution over feasible sets.
The Limitations of Optimization from Samples
In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution.
