orange3xchicken

You probably couldn't find much evidence re Krugman + Charlatan, because he's not. He's an accomplished economist who's influential and respected among the academic community. He's won two of the most prestigious awards in economics (Nobel + John Bates Clark). He's had some peculiar arguments in the past, which were dumb imo, but nothing "Charlatan"... Science is hard and economics is unique. Krugman is known for doing post-mortems and admitting when his ideas are off. I also think that being recognized by the academic community and his peer economists is a reasonable signal that he's not an idiot.

More recently he's been a bit more into hackish punditry and is prob. most famous among regular people for his somewhat abrasive nyt column. I think your idea about and API is not ideal. Is it enough to trust some randos on hacker news to identify Charlatans?

At least on the quant side, I think the typical sentiment is that most researchers aren't interested in ops / developing infrastructure / curating datasets.

It sounds like you aren't really interested in a rational discussion by the second half of your post, but the typical arguments (incl in the post) for are that market makers reduce inefficiencies in the market & provide liquidity that significantly reduces the bar (i.e. make trading cheaper) for retail investors (like you or me) to trade.

I think it is generally accepted that society does benefit from a modern and efficiently run market. Whether or not automated market makers contribute to this could be up for debate, I guess.

I know, I've been waiting so long for this! I will not miss having to render and reference pngs of equations / api requests.

I think K is usually interpreted as the # of gaussians from which the data has been assumed to be sampled. Not immediately related to # of latent variables unless you invoke kernel kmeans or something like laplacian eigenmaps/diffusion maps.

The term "labeled graph" just means a graph with each node labeled differently (but arbitrarily). It just allows for reasoning & enumerating the vertex set. It's a typical assumption to make in the context of graph isomorphism.

It doesn't relate to machine learning (which is what I assume you mean).

That's really cool. These days similar strategies based on graph coarsening and vertex ordering are really popular for improving the sparsity pattern of preconditioners- e.g. incomplete lu decompositions for iterative linear solvers.

A new subfield of adversarial ML that considers similar challenges to adversarial NLP: topological attacks on graphs for attacking graph/node classifiers.

Both problems (NLP & graph robustness) are made much more challenging compared to adversarial robustness/attacks on image classifiers due to their combinatorial nature.

For graphs, canonical notions of robustness wrt classes of perturbations defined based on lp norms aren't so great (e.g. consider perturbing a barbell graph by removing a bridge edge- huge topological perturbation, but tiny lp perturbation!)

I think investigating robustness for graph classifiers should also help robustness for practical nlp systems and visa-versa. For example, is there any work that investigates robustness of nlp systems, but considers classes of perturbations defined on the space of ASTs?

Recommend Russel's Human Compatible. Three principles to guide AI development:

1. The machine's only objective is to maximize the realization of human preferences.

2. The machine is initially uncertain about what those preferences are but then is.

3. The ultimate source of information about human preferences is human behavior.

Maybe Bosch? Prof. Zico Kolter from CMU is a chief scientist associated with them, and his group does a lot of really good work in the ml verification space (e.g. the first randomized smoothing and the Wong & Kolter certificates).