We consider the problem of allocating $m$ indivisible chores to $n$ agents with additive disvaluation (cost) functions. It is easy to show that there are picking sequences that give every agent (that uses the greedy picking strategy) a bundle of chor...
Sep 7, 2020 · How do people not realize that Donald Trump is objectively stupid? I'm not sure he is objectively stupid. He's a greedy, corrupt, morally bankrupt narcissist who isn't fit to be in charge of a …
Resilient Big Data monetization is devised as k-dominance and m-connectivity problems, such that common-interests are connected by k-ways to measurement tools, which are tied within each other in m-ways. Consequently, a greedy approximation algorithm...
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Early-Exit Deep Neural Networks enable adaptive inference by allowing prediction at intermediary layers, significantly reducing computational costs and latency. Most of the early exit strategies greedily exit a sample at an intermediary layer if the...
The Grundy number of a graph is the maximum number of colors used by the greedy coloring algorithm over all vertex orderings. In this paper, we study the computational complexity of GRUNDY COLORING, the problem of determining whether a given graph ha...
KPMG preformed my company’s financial audit and they let everything fly as long as you throw a stack of cash in their greedy little faces… Only reason I bring this up is because I saw some dude g...
We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds elements from a stream either to a solution set $S$ or to an extra bu...
explored a more pop-oriented sound and produced the multi-platinum single "Greedy", which became one of the bestselling songs of 2024 globally. In 2025, McRae
Dec 27, 2012 · Did Microsoft license this component from a vendor, then get greedy and decide to cut the vendor out of its percentage in the next release? Did Microsoft hope to accelerate user adoption …
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision t...
We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation fact...
In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples...
The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a whole, and hence, often struggles to capture biases caused by the page l...
Dec 27, 2012 · Did Microsoft license this component from a vendor, then get greedy and decide to cut the vendor out of its percentage in the next release? Did Microsoft hope to accelerate user adoption …
Various unsupervised greedy selection methods have been proposed as computationally tractable approximations to the NP-hard subset selection problem. These methods rely on sequentially selecting the variables that best improve performance with respec...
The paper presents a topology-agnostic greedy protocol for network-on-chip routing. The proposed routing algorithm can tolerate any number of permanent faults, and is proven to be deadlock-free. We introduce a specialized variant of the algorithm, wh...
We consider the problem of scheduling in multihop wireless networks subject to interference constraints. We consider a graph based representation of wireless networks, where scheduled links adhere to the K-hop link interference model. We develop a di...
One approach to estimating a species tree from a collection of gene trees is to first estimate probabilities of clades from the gene trees, and then to construct the species tree from the estimated clade probabilities. While a greedy consensus algori...
A heuristic greedy algorithm is developed for efficiently tiling spatially dense redshift surveys. In its first application to the Galaxy and Mass Assembly (GAMA) redshift survey we find it rapidly improves the spatial uniformity of our data, and n...