Tina Eliassi-Rad

You're trying to understand what is going on, what is the underlying process that is happening in this network and why these links exist. Now, the one thing that makes studying of graphs and networks really interesting is that it is not a closed world. So just because you didn't see a link between me and Jennifer doesn't mean that we're not friends.

You're trying to understand what is going on, what is the underlying process that is happening in this network and why these links exist. Now, the one thing that makes studying of graphs and networks really interesting is that it is not a closed world. So just because you didn't see a link between me and Jennifer doesn't mean that we're not friends.

And so for machine learning where you need both positive examples and both negative examples, which negative examples do you pick becomes difficult because the edges or the links or the friendships that don't exist may because like they don't want to be friends or for other reasons. And so this what are the negative examples becomes an important aspect of things.

And so for machine learning where you need both positive examples and both negative examples, which negative examples do you pick becomes difficult because the edges or the links or the friendships that don't exist may because like they don't want to be friends or for other reasons. And so this what are the negative examples becomes an important aspect of things.

Indeed, indeed. So there are lots of assumptions being made, obviously, in terms of like how the network is being observed. And in fact, this is one of the big differences between computer scientists and

Indeed, indeed. So there are lots of assumptions being made, obviously, in terms of like how the network is being observed. And in fact, this is one of the big differences between computer scientists and

that study graphs and network scientists that are typically physicists or social scientists, where, for example, they're like, well, there's a distribution and this graph fell from it versus like the machine learning graph mining folks typically don't question where the graph came from. They're like, oh, here's data and they run with it. Right.

that study graphs and network scientists that are typically physicists or social scientists, where, for example, they're like, well, there's a distribution and this graph fell from it versus like the machine learning graph mining folks typically don't question where the graph came from. They're like, oh, here's data and they run with it. Right.

And it's just it boggles the mind that like you should think about where this data came from, how it was collected, What were maybe the errors in collecting it? And in fact, this touches on a sore point for me because what happens is they don't question the data, right? They just like feed it into their machine learning AI models. And then on the other end, they don't measure any uncertainty.

And it's just it boggles the mind that like you should think about where this data came from, how it was collected, What were maybe the errors in collecting it? And in fact, this touches on a sore point for me because what happens is they don't question the data, right? They just like feed it into their machine learning AI models. And then on the other end, they don't measure any uncertainty.

So like if you have something like, let's say, a social network that you've observed, there's all this stuff about like representation learning, right? Where basically I take Tina in the social network and I represent her as a vector in a Euclidean space, right? Like maybe with 60,000, a vector with 16,000 elements in it. So the cardinality is 16,000 and there's no uncertainty.

So like if you have something like, let's say, a social network that you've observed, there's all this stuff about like representation learning, right? Where basically I take Tina in the social network and I represent her as a vector in a Euclidean space, right? Like maybe with 60,000, a vector with 16,000 elements in it. So the cardinality is 16,000 and there's no uncertainty.

They're like, no, Tina falls exactly here and it just doesn't make sense at all, right? And so then those kinds of models, given that, You didn't start with, okay, well, my data could have some noise in it, some uncertainty in it. And then you don't even capture the uncertainty of the model at the end.

They're like, no, Tina falls exactly here and it just doesn't make sense at all, right? And so then those kinds of models, given that, You didn't start with, okay, well, my data could have some noise in it, some uncertainty in it. And then you don't even capture the uncertainty of the model at the end.

It just, there are lots of problems that can occur, including, for example, adversarial attacks or like your model is not just going to be, your model is not going to be robust. Let's just put it that way.

It just, there are lots of problems that can occur, including, for example, adversarial attacks or like your model is not just going to be, your model is not going to be robust. Let's just put it that way.

Yeah, I think in part, one of the reasons that folks, at least in the CS side, the computer science and the machine learning side, aren't too bothered by it these days is because we are going through this era where prediction is everything. Prediction and accuracy is everything. And so, you know, there are these benchmarks and it's basically benchmark hacking or state of the art hacking, right?

Yeah, I think in part, one of the reasons that folks, at least in the CS side, the computer science and the machine learning side, aren't too bothered by it these days is because we are going through this era where prediction is everything. Prediction and accuracy is everything. And so, you know, there are these benchmarks and it's basically benchmark hacking or state of the art hacking, right?

And that's basically what is going on. You know, that's the reality of it, you know. And and so so there's a lot of that kind of engineering going on as opposed to like really thinking about what is the phenomena that I'm interested in? How is the data coming to me? What are the sources of noise? Should I how should I take them into account? Should I even take them into account?

And that's basically what is going on. You know, that's the reality of it, you know. And and so so there's a lot of that kind of engineering going on as opposed to like really thinking about what is the phenomena that I'm interested in? How is the data coming to me? What are the sources of noise? Should I how should I take them into account? Should I even take them into account?