4 ms·

I work in the ICU monitoring field, on the R&D team of a company with live systems at dozens of hospitals and multiple FDA approvals. We use extended Kalman fil

by wswope 2y ago

I work in the ICU monitoring field, on the R&D team of a company with live systems at dozens of hospitals and multiple FDA approvals. We use extended Kalman filters (i.e. non-blackbox "ML") to estimate certain lab values of patients that are highly indicative of them crashing, based on live data from whatever set of monitors they're hooked up to - and it's highly robust. What the authors of this paper are doing is throwing stuff at the wall to see if it works, and publishing results. That's not necessarily a bad thing at all, but I say this to underline that their results are not at all reflective of SOTA capabilities, and they're not doing much exploration of prior art.

jvanderbot 2y ago
Parameter estimation is ML now?
- klodolph 2y ago
  I think ML is in quotes for a reason—the reason is because the usage is not typical.
- thenobsta 2y ago
  Why not? LLMs, vision models, and kalman filters all learn parameters based on data.
  - PaulHoule 2y ago
    A linear regression model can be written and trained as a neural net, has a loss function, all of that. Most if not all ML problems can be formulated as modelling a probability distribution
    - klodolph 2y ago
      That’s too reductive—ML models are statistical models. Statistical models have parameters, and in general cases, you choose the parameters with some kind of optimization algorithm. If you play fast and loose with your definition of “ML”, you’ll end up defining it so that any statistical model is an ML model… in which case, why even bother using two different terms? ML models are, broadly speaking, the more complicated ones with more parameters, where the behavior of the models is not really known without training. I’m sure you could nitpick to death any definition I give, but that’s fine.
      - PaulHoule 2y ago
        I am sure there are people teach data science classes who look at it in that "reductive" way. From the viewpoint of engineering, scikit-learn provides the same interface to linear regression that it supplies to many other models. Huggingface provides an interface to models that is similar in a lot of ways but I think a 'regression' in that it doesn't provide the bare minimum of model selection facilities needed to reliably make calibrated models. There are many problems where you could use either linear regression or a much more complex model. When it comes to "not known without training" I'm not sure how much of that is the limit of what we know right now and how much is fundamental as in the problem of "we can't really know what a computer program with free loops with do" (Halting problem) or "we can't predict what side of the side Pluto is going to be on in 30 million years" (Deterministic chaos) (The first industrial model trainer I built was a simultaneously over and under engineered mess like most things in this industry... I didn't appreciate scikit-learn's model selection facilities and even though the data sci's I worked with had a book understanding of them, they didn't really put them to work.)
        klodolph 2y ago
        There’s a pedagogical reason to teach things with a kind of reductive definition. It makes a lot of sense. I remember getting cornered by somebody in a statistics class and interrogated about whether I thought neural networks were statistical techniques. In that situation I’ll only answer yes, they are statistical techniques. As far as I can tell, a big chunk of what we do with machine learning is create complicated models with a large number of parameters. We’re not creating something other than statistical models. We just draw a kind of cultural line between traditional statistics and machine learning techniques. Now that I think about it, maybe if you asked me about the line between traditional statistics and machine learning, I would say that in traditional statistics, you can understand the parameters.
        blackbear_ 2y ago
        > in traditional statistics, you can understand the parameters. I also think that this is the key differentiator between ML and stats. Statistical models can be understood formally, which means that not only we know how each parameter affects the predictions, we also know what their estimation uncertainties are, under which assumptions, and how to check that these assumptions are satisfied. Usually, we value these models not only because they're predictive but also because they're interpretable. In ML there is neither the luxury nor the interest in doing this, all we want is something that predicts as well as possible. So the difference is not the model itself but what you want to get out of it.
    - jvanderbot 2y ago
      You've equated neural networks with ML. I'm questioning the association of a single maximum likelihood parameter estimation via analytical optimization as ML, because it does not invole any methods beyond calculus and no models other than the system itself, whose parameters we are estimating. Perhaps I'm wrong, but the power of NN is in an unknown intermediate representation between the data (measurements in estimation) and the prediction. EKF has no such black box. Which now that I've written it agrees with top level comment so I rescind my question.
- windsignaling 2y ago
  Neural networks are not ML now?
  - jvanderbot 2y ago
    EKF is a neural network!?
    - windsignaling 2y ago
      I think you missed the point of that comment. I was responding to the comment saying "Parameter estimation is ML now?" Neural networks are trained commonly using maximum likelihood estimation, a common parameter estimation technique.
AlotOfReading 2y ago
Calling EKFs "ML" is certainly a choice.
- Epa095 2y ago
  Is it less ML than linear regression?
  - klodolph 2y ago
    If you want to draw the line between ML and not ML, I think you’ll have to put Kalman filters and linear regression on the non-ML side. You can put support vector machines and neural networks on the ML side. In some sense the exact place you draw the distinction is arbitrary. You could try to characterize where the distinction is by saying that models with fewer parameters and lower complexity tend to be called “not ML”, and models with more parameters and higher complexity tend to be called “ML”.
    - wrsh07 2y ago
      Linear regression is literally the second lecture of the Stanford ML class. https://cs229.stanford.edu/ https://cs229.stanford.edu/ If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
      - klodolph 2y ago
        When you say it’s “obviously machine learning”, how could that statement possibly be correct? There’s not even broad consensus here… so you don’t get to say that your definition is obviously correct. There are pedagogical reasons why you’d include linear regression in a machine learning course. This is pretty clear to me—they have properties which are extremely important to the field of machine learning field, such as differentiability.
        computerex 2y ago
        Linear regression is ML. You are off base.
nyrikki 2y ago
As an intuition on why many people see this as different. PAC Learning is about compression, KF/EKF is more like Taylor expansion. The specific types of PAC Learning that this paper covers has problems with a simplicity bias, and fairly low sensitivity. While based on UHATs, this paper may provide some insights. https://arxiv.org/abs/2502.02393 https://arxiv.org/abs/2502.02393 Obviously LLM and LRMs are the most studied, but even the recent posts on here from anthropic show that without a few high probability entries in the k-top results, confabulations are difficult for transformers. Obviously there are PAC Learning methods that target anomaly detection, but they are very different than even EKF + Mc You will note in this paper that even highly weighted features exhibited low sensitivity. While the industry may find some pathological cases that make the approach usable, autograd and the need for parallelism make the application of this papers methods to tiny variations to multivariate problem ambitious. They also only trained on medical data. Part of the reason the foundation models do so well is that they encode verifiers from a huge corpus that invalidates the traditional bias variance tradeoffs from the early 90's papers. But they are still selecting from the needles and don't have access to the hay in the haystack. The following paper is really not related except it shows how compression exacerbates that problem. https://arxiv.org/abs/2205.06977 https://arxiv.org/abs/2205.06977 Chaitin's constant encoding the Halting problem, and that it is normal and uncomputable is the extreme top end of computability, but relates to the compression idea. EKFs have access to the computable reals, and while non-linear, KF and EKFs can be thought of linearization of the approximations as a lens. If the diagnostic indicators were both ergodic and Markovian, this paper's approach would probably be fairly reliable. But these efforts are really about finding a many to one reduction that works. I am skeptical about it in this case for PAC ML, but perhaps they will find a pathological case. But the tradeoffs between statistical learning and expansive methods are quite different. Obviously hype cycles drive efforts, I encourage you to look at this years AAAI conference report and see that you are not alone with the frustration on the single minded approach. IMHO this paper is a net positive, showing that we are moving from a broad exploration to targeted applications. But that is just my opinion.

by wswope 2y ago

jvanderbot 2y ago

Parameter estimation is ML now?

klodolph 2y ago
I think ML is in quotes for a reason—the reason is because the usage is not typical.
thenobsta 2y ago
Why not? LLMs, vision models, and kalman filters all learn parameters based on data.
- PaulHoule 2y ago
  A linear regression model can be written and trained as a neural net, has a loss function, all of that. Most if not all ML problems can be formulated as modelling a probability distribution
  - klodolph 2y ago
    That’s too reductive—ML models are statistical models. Statistical models have parameters, and in general cases, you choose the parameters with some kind of optimization algorithm. If you play fast and loose with your definition of “ML”, you’ll end up defining it so that any statistical model is an ML model… in which case, why even bother using two different terms? ML models are, broadly speaking, the more complicated ones with more parameters, where the behavior of the models is not really known without training. I’m sure you could nitpick to death any definition I give, but that’s fine.
    - PaulHoule 2y ago
      I am sure there are people teach data science classes who look at it in that "reductive" way. From the viewpoint of engineering, scikit-learn provides the same interface to linear regression that it supplies to many other models. Huggingface provides an interface to models that is similar in a lot of ways but I think a 'regression' in that it doesn't provide the bare minimum of model selection facilities needed to reliably make calibrated models. There are many problems where you could use either linear regression or a much more complex model. When it comes to "not known without training" I'm not sure how much of that is the limit of what we know right now and how much is fundamental as in the problem of "we can't really know what a computer program with free loops with do" (Halting problem) or "we can't predict what side of the side Pluto is going to be on in 30 million years" (Deterministic chaos) (The first industrial model trainer I built was a simultaneously over and under engineered mess like most things in this industry... I didn't appreciate scikit-learn's model selection facilities and even though the data sci's I worked with had a book understanding of them, they didn't really put them to work.)
      - klodolph 2y ago
        There’s a pedagogical reason to teach things with a kind of reductive definition. It makes a lot of sense. I remember getting cornered by somebody in a statistics class and interrogated about whether I thought neural networks were statistical techniques. In that situation I’ll only answer yes, they are statistical techniques. As far as I can tell, a big chunk of what we do with machine learning is create complicated models with a large number of parameters. We’re not creating something other than statistical models. We just draw a kind of cultural line between traditional statistics and machine learning techniques. Now that I think about it, maybe if you asked me about the line between traditional statistics and machine learning, I would say that in traditional statistics, you can understand the parameters.
        blackbear_ 2y ago
        > in traditional statistics, you can understand the parameters. I also think that this is the key differentiator between ML and stats. Statistical models can be understood formally, which means that not only we know how each parameter affects the predictions, we also know what their estimation uncertainties are, under which assumptions, and how to check that these assumptions are satisfied. Usually, we value these models not only because they're predictive but also because they're interpretable. In ML there is neither the luxury nor the interest in doing this, all we want is something that predicts as well as possible. So the difference is not the model itself but what you want to get out of it.
  - jvanderbot 2y ago
    You've equated neural networks with ML. I'm questioning the association of a single maximum likelihood parameter estimation via analytical optimization as ML, because it does not invole any methods beyond calculus and no models other than the system itself, whose parameters we are estimating. Perhaps I'm wrong, but the power of NN is in an unknown intermediate representation between the data (measurements in estimation) and the prediction. EKF has no such black box. Which now that I've written it agrees with top level comment so I rescind my question.
windsignaling 2y ago
Neural networks are not ML now?
- jvanderbot 2y ago
  EKF is a neural network!?
  - windsignaling 2y ago
    I think you missed the point of that comment. I was responding to the comment saying "Parameter estimation is ML now?" Neural networks are trained commonly using maximum likelihood estimation, a common parameter estimation technique.

AlotOfReading 2y ago

Calling EKFs "ML" is certainly a choice.

Epa095 2y ago
Is it less ML than linear regression?
- klodolph 2y ago
  If you want to draw the line between ML and not ML, I think you’ll have to put Kalman filters and linear regression on the non-ML side. You can put support vector machines and neural networks on the ML side. In some sense the exact place you draw the distinction is arbitrary. You could try to characterize where the distinction is by saying that models with fewer parameters and lower complexity tend to be called “not ML”, and models with more parameters and higher complexity tend to be called “ML”.
  - wrsh07 2y ago
    Linear regression is literally the second lecture of the Stanford ML class. https://cs229.stanford.edu/ https://cs229.stanford.edu/ If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
    - klodolph 2y ago
      When you say it’s “obviously machine learning”, how could that statement possibly be correct? There’s not even broad consensus here… so you don’t get to say that your definition is obviously correct. There are pedagogical reasons why you’d include linear regression in a machine learning course. This is pretty clear to me—they have properties which are extremely important to the field of machine learning field, such as differentiability.
      - computerex 2y ago
        Linear regression is ML. You are off base.

nyrikki 2y ago

As an intuition on why many people see this as different. PAC Learning is about compression, KF/EKF is more like Taylor expansion. The specific types of PAC Learning that this paper covers has problems with a simplicity bias, and fairly low sensitivity. While based on UHATs, this paper may provide some insights. https://arxiv.org/abs/2502.02393 https://arxiv.org/abs/2502.02393 Obviously LLM and LRMs are the most studied, but even the recent posts on here from anthropic show that without a few high probability entries in the k-top results, confabulations are difficult for transformers. Obviously there are PAC Learning methods that target anomaly detection, but they are very different than even EKF + Mc You will note in this paper that even highly weighted features exhibited low sensitivity. While the industry may find some pathological cases that make the approach usable, autograd and the need for parallelism make the application of this papers methods to tiny variations to multivariate problem ambitious. They also only trained on medical data. Part of the reason the foundation models do so well is that they encode verifiers from a huge corpus that invalidates the traditional bias variance tradeoffs from the early 90's papers. But they are still selecting from the needles and don't have access to the hay in the haystack. The following paper is really not related except it shows how compression exacerbates that problem. https://arxiv.org/abs/2205.06977 https://arxiv.org/abs/2205.06977 Chaitin's constant encoding the Halting problem, and that it is normal and uncomputable is the extreme top end of computability, but relates to the compression idea. EKFs have access to the computable reals, and while non-linear, KF and EKFs can be thought of linearization of the approximations as a lens. If the diagnostic indicators were both ergodic and Markovian, this paper's approach would probably be fairly reliable. But these efforts are really about finding a many to one reduction that works. I am skeptical about it in this case for PAC ML, but perhaps they will find a pathological case. But the tradeoffs between statistical learning and expansive methods are quite different. Obviously hype cycles drive efforts, I encourage you to look at this years AAAI conference report and see that you are not alone with the frustration on the single minded approach. IMHO this paper is a net positive, showing that we are moving from a broad exploration to targeted applications. But that is just my opinion.