Top Tweets for #MLforOPT
Call for Papers: "#AI for Decision #Optimization" workshop at @AAAI 2022. Deadline *Nov 12* for long/short papers, extended abstracts, software demos, and a fast track from NeurIPS and AAAI. https://t.co/aEdLakKGDG #MLforOPT @69alodi @SegevWasserkrug
I am so proud of @lyeskhalil for being recognized with a Canadian Scale AI Research Chair. So well deserved! Looking forward to the great advancements his research group will make on data-driven algorithm design #MLforOPT
Excited to lead the Scale AI Research Chair in Data-Driven Algorithms for Modern Supply Chains @uoftmie @uoftengineering for the next 5 years! Thank you @AISupplyChain & @UofT for the support, which will enable opportunities for graduate and postdoc researchers in ML x OR x SCM
This #NeurIPS2021 competition will help push forward progress on learning-driven algorithm design for combinatorial optimization #MLforOPT
[1/3] The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims at improving state-of-the-art combinatorial optimization solvers by replacing / integrating key heuristic components with machine learning models.
Combining AI & Optimization. TSP competition as part of @IJCAIconf 2021
https://t.co/ccwAqDBNfC
#cp #ai #ml #orms
Today 9pm-11pm PST @NeurIPSConf poster: our work on #MLforOPT, using ML to learn Large Neighborhood Search for #MIP, lead by Jialin Song, with Ravi Lanka and @yisongyue
A simple recipe for solving Integer Programs quickly:
1) Sample a decomposition
2) Run black-box solver (eg Gurobi) per component
3) Repeat
Can be *much* faster than running Gurobi on whole problem!
@NeurIPSConf Poster Session: https://t.co/hSkSJ0yjKW (9-11pm PST today!)

While most of our work has focused on leveraging ML to improve discrete optimization methods #MLforOPT, this paper uses combinatorial solving for fitting BNN predictive models
Check out our new paper: Combinatorial Attacks on Binarized Neural Networks https://t.co/DTxjs8NpcF! Joint with my lab mate @itsamritagupta and our adviser @BDilkina
Giulia Zarpellon @MartiFischetti and @69alodi use #ML to predict whether a MIP solver will converge before the timelimit or not #ISMP2018

#MLforOPT talks on Tuesday @lyeskhalil @69alodi @mluebbecke, #compsust talks on Thursday @fangf07 @jnahid
Ready to welcome all ISMP'2018 participants in Bordeaux!

Great master class on “data science meets combinatorial optimization” at #cpaior2018 today. Was happy to present our work on ML for MIP branch and bound #MLforOPT
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