Meeting w/ Abhishek

[hjsuh94]

1. The fundamental claim is that score matching / diffusion models can draw you to within-data distribution regimes.

• what is the extent to which I can get o.o.d. before diffusion models start to be less effective?
• especially on high dimensions, is this true?
• people have thought about doing this for anomaly detection, maybe it's interesting to make connections:
• https://openreview.net/forum?id=5tKhUU5WBi8
• https://arxiv.org/pdf/2211.07740.pdf
• This claims is generalizable beyond the setting of model-based offline RL, and would apply to any kind of model-based optimization? (where we have uncertainty over cost functions)

2. Why use score-based generative models as opposed to ensembles?

• need a persuasive example that we can handle something that ensembles cannot handle.
• epistemic vs. aleatoric uncertainty. How well do generative models do for either?
• https://cims.nyu.edu/~andrewgw/deepensembles/

TODOs:

• Produce a simple example of a diffusion-based score field trying to pull samples within distribution.
• Produce a simple example where diffusion-based score fields to do better than ensembles.
• Compare with CQL: why do model-based?

Benchmarks:

• If we have some convincing cases where this beats ensembles, might not be too necessary.