Questions tagged [optimization]
In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint density (or mass function) of the observed data referred to as Maximum Likelihood Estimation (MLE).
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Recommendations for a machine learning trading system [closed]
I recently completed a project that I have been working on to predict the movement of a stocks price either up or down then using a portfolio optimization algorithm to create a expected sharpe ratio ...
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Encouraging sparsity at block level or element-wise level?
I have an objective function $f(W)$, where $W$ is a $Kp \times Kp$ matrix. We can view $W$ is a $p \times p$ block matrix, where each block has the dimension $K \times K$. Now to optimize $f(W)$, I ...
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LeaveOneOut CV for Bandwidth selection of Kernel Density Estimation
I've taken this code in order to try optimization of bandwidth_selection with GridSearchCV (while implementing LeaveOneOut logics within this CV: "LeaveOneOut() is equivalent to KFold(n_splits=n)&...
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How does seeing training batches only once influence the generalization of a neural network?
I am referring to this question/scenario Train neural network with unlimited training data but unfortunately I can not comment.
As I am not seeing any training batch multiple times I would guess that ...
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Is there a max absolute error loss function?
I'm fitting a small model and need to use a custom loss function. I want to avoid large errors. I thought of a max absolute error function
$$ \text{MaxAE} = \max(|y-y_{pred}|) \, ,$$
with y the ...
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Optimal combination of feature values to maximize a metric
I'm facing a complex issue with a project I'm working on and would appreciate some advice. Here's the context:
We have a search engine where users input queries and get results filtered through ...
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Optimal combination of variables to minimise output
To be honest I'm not 100% sure how much this is purely a coding issue or a data science issue, but I'll take my chances. I've developed a matrix which is a mixture of various hyperparameters, the ...
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Using Reinforcement learning for minimisation
I would like to use reinforcement learning for the optimisation of a given function under some contraints.
Take for example the following problems:
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How to comment on goodness of loss functions?
I have two loss functions $\mathcal{L}_1$ and $\mathcal{L}_2$ to train my model. The model is predominantly a classification model. Both $\mathcal{L}_1$ and $\mathcal{L}_2$ takes are two variants of ...
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Each person gets their top, or second choice of activity over a period of 6 slots
We are running a camp for 130 children, and on 3 days they can pick different activities to do. One activities for slot 1 (45min), the other for slot 2 (another 45min), enabling them to do 6 ...
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Bulding Deep Learning model for multiclassification case
I am soo confused i read a lot of information in forumas and still cna't get what is wrong.
my data is around 500.000 rows and 32 columns. my target variables consists of 3 classes (0, 1, 2). Hyperopt ...
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Learning the gradient descent stepsize with RL [closed]
Problem statement:
I've been working on a project to accelerate the convergence of gradient descent using reinforcement learning (RL). I want to learn a policy that can map the current state of ...
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How to prevent update a pretrained model if a model is optimized with backpropagation in Pytorch?
I use Pytorch exclusively to develop my model, and these are components in my model and how it works:
A generator
An encoder: a pretrained, and should not updated.
A loss function.
Input is passed to ...
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Learning the "surface" of a function
Given a continuous non-convex function and assuming knowledge of all extremum points, is it possible to learn all initialization points from which performing classic GD lead to global minimum? Pure ...
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How to force my model heads to learn different things?
I have an Seq2Seq model that has 2 generative LM heads. I want the two heads to focus on different features/styles while decoding. The approach that I was thinking of is adding a distance cost to the ...
