Setting up robust optimization experiment with MVaR gives error about ax

[sdaulton]

MVaR is not differentiable, so gradient issues not terribly surprising.

To get unblocked on this, a recommended alternative is to use MARS (https://proceedings.mlr.press/v162/daulton22a.html) which is way faster and differentiable than directly optimizing MVaR with qNEHVI. You can use MARS by instead setting

risk_measure = RiskMeasure( risk_measure="MARS", options={"n_w": 16, "alpha": 0.8}, )
and

modelbridge = Models.BOTORCH_MODULAR(
        experiment=exp,
        data=exp.fetch_data(),
        surrogate=Surrogate(botorch_model_class=SingleTaskGP),
        botorch_acqf_class=qLogNoisyExpectedImprovement,
)
```.

[apaleyes]

Thanks, @sdaulton , that unblocked me indeed! Can I ask why your code uses qLogNoisyExpectedImprovement and not its hypervolume counterpart?

@saitcakmak glad it reproduced, thanks for responding with the fix so quickly

[saitcakmak]

Hi @apaleyes. The code you shared runs fine for me, on Ax 0.3.6. I don't think there were any changes to this part of the code recently, so I don't know why you'd be getting an error due to gradients.

Can you try again with the latest versions of Ax & BoTorch? If you get the error again, sharing the full stack trace could be helpful to identify where the error is coming from.

[saitcakmak]

Oh, I copy pasted the code and didn't realize that it was using expectation rather than MVaR. I can reproduce the issue after updating that

[saitcakmak]

Ok, the issue is that the MVaR implementation in BoTorch is not differentiable. The code has a warning on this but it is easy to miss when you get an error: https://github.com/pytorch/botorch/blob/main/botorch/acquisition/multi_objective/multi_output_risk_measures.py#L498-L505

We do have a version of it with approximate gradients but looks like that change was never upstreamed to BoTorch.

[sdaulton]

Glad that unblocked you! MARS optimizes MVaR by optimizing the VaR of random Chebyshev scalarizations. Since it scalarizes the problem, it uses a single-objective acquisition function.