Smart “predict, then optimize”
AN Elmachtoub, P Grigas - Management Science, 2022 - pubsonline.informs.org
Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …
optimization. Because of the typically complex nature of each challenge, the standard …
Competitive caching with machine learned advice
T Lykouris, S Vassilvitskii - Journal of the ACM (JACM), 2021 - dl.acm.org
Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …
Submodularity in machine learning and artificial intelligence
J Bilmes - arXiv preprint arXiv:2202.00132, 2022 - arxiv.org
In this manuscript, we offer a gentle review of submodularity and supermodularity and their
properties. We offer a plethora of submodular definitions; a full description of a number of …
properties. We offer a plethora of submodular definitions; a full description of a number of …
Scheduling with predictions and the price of misprediction
M Mitzenmacher - arXiv preprint arXiv:1902.00732, 2019 - arxiv.org
In many traditional job scheduling settings, it is assumed that one knows the time it will take
for a job to complete service. In such cases, strategies such as shortest job first can be used …
for a job to complete service. In such cases, strategies such as shortest job first can be used …
The adaptive complexity of maximizing a submodular function
E Balkanski, Y Singer - Proceedings of the 50th annual ACM SIGACT …, 2018 - dl.acm.org
In this paper we study the adaptive complexity of submodular optimization. Informally, the
adaptive complexity of a problem is the minimal number of sequential rounds required to …
adaptive complexity of a problem is the minimal number of sequential rounds required to …
Limits of optimization
C Carissimo, M Korecki - Minds and Machines, 2024 - Springer
Optimization is about finding the best available object with respect to an objective function.
Mathematics and quantitative sciences have been highly successful in formulating problems …
Mathematics and quantitative sciences have been highly successful in formulating problems …
Customizing ML predictions for online algorithms
A popular line of recent research incorporates ML advice in the design of online algorithms
to improve their performance in typical instances. These papers treat the ML algorithm as a …
to improve their performance in typical instances. These papers treat the ML algorithm as a …
Profit maximization for viral marketing in online social networks: Algorithms and analysis
Information can be disseminated widely and rapidly through Online Social Networks (OSNs)
with “word-of-mouth” effects. Viral marketing is such a typical application in which new …
with “word-of-mouth” effects. Viral marketing is such a typical application in which new …
Efficient and thrifty voting by any means necessary
We take an unorthodox view of voting by expanding the design space to include both the
elicitation rule, whereby voters map their (cardinal) preferences to votes, and the …
elicitation rule, whereby voters map their (cardinal) preferences to votes, and the …
Parallelizing greedy for submodular set function maximization in matroids and beyond
We consider parallel, or low adaptivity, algorithms for submodular function maximization.
This line of work was recently initiated by Balkanski and Singer and has already led to …
This line of work was recently initiated by Balkanski and Singer and has already led to …