Nice speech and excellent work! I wonder if the framework is suitable for situations when there are many unknown parameters like demand, load, and so on.
Amazing work. Ran into this same problem many times. Ur generalization is amazing. D. U have any portfolio optimization or some form of knapsack problem example?
Code with the paper is here which has portfolio opt : github.com/paulgrigas/SmartPredictThenOptimize But this recent work is the place to go and has knapsack I believe: github.com/khalil-research/PyEPO
It seems like the OR researchers seldom share their code and data. I could understand that this require extra efforts. Really appreciate this piece of work, a lot of amazing ideas.
@@adamelmachtoub2140 Very nice work! I am a researcher on causality. The prediction part corresponds to the ITE estimation, while the optimization part corresponds to the policy learning. Your work inspires a promising future for causal policy research! Thanks a lot~
Nice speech and excellent work!
I wonder if the framework is suitable for situations when there are many unknown parameters like demand, load, and so on.
Amazing work. Ran into this same problem many times. Ur generalization is amazing. D. U have any portfolio optimization or some form of knapsack problem example?
Code with the paper is here which has portfolio opt : github.com/paulgrigas/SmartPredictThenOptimize But this recent work is the place to go and has knapsack I believe: github.com/khalil-research/PyEPO
Read the paper and this presentation was amazing.
I was wondering where can I find the codes of the findings (and if you used python)?
It seems like the OR researchers seldom share their code and data. I could understand that this require extra efforts.
Really appreciate this piece of work, a lot of amazing ideas.
Code with the paper is here: github.com/paulgrigas/SmartPredictThenOptimize But this recent work is the place to go: github.com/khalil-research/PyEPO
@@adamelmachtoub2140 Very nice work! I am a researcher on causality. The prediction part corresponds to the ITE estimation, while the optimization part corresponds to the policy learning. Your work inspires a promising future for causal policy research! Thanks a lot~
Sorry did not notice your research until now.