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### Which optimization algorithm to use for problems with many local optima and expensive goal function? [duplicate]

I've got an optimization problem with these properties: The goal function is not cheap to compute. It can be evaluated up to around 10^4 times in the optimization. There's a lot of local optima. High ...
750 views

### Neural Network for input values optimization [duplicate]

I have electric machine, which parameters I measure by 10 sensors. 8 of them measures "input" values and 2 of them result (output). I've got tons of historical data of all of these sensors. I built a ...
220 views

### Tuning of Hyperparameter [duplicate]

Are there any advanced packages that allows automated tuning of hyperparameters for neural network and traditional machine learning algorithms like XGBoost, random forest (using method like Bayesian, ...
90 views

### Optimization algorithm where calculating the function is very costly [duplicate]

I am doing physics simulation and have to minimize a function $f$ of approx. 10 parameters $f=f(x_1, x_2, ... , x_{10} )$. I have already looked into several optimization algorithms (downhill-simplex, ...
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### Maximize slow function [duplicate]

So from experience (don't judge me, it was years ago...) I know that when a person with little knowledge about a field try to explain a problem to someone with a lot of experience it can easily lead ...
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I have a non-convex function of several parameters, that takes real values. I am looking for a good algorithm to find local minima. I am reasonably confident I can come up with descent starting points....
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### Is there any method for learning regularization parameter? [duplicate]

One of the hardest tasks in using machine learning methods is choosing the appropriate hyper-parameters of the model such as regularization parameter. As far as I know, this task is performed by a ...
16 views

### Deriving cost function [duplicate]

I have calculated data from a computer simulation for a wireless network as shown below, here P is an algorithm tuning parameter, 'C' and 'D' are % decrease in collisions and % decrease in delay ...
9k views

### Why should I be Bayesian when my model is wrong?

Edits: I have added a simple example: inference of the mean of the $X_i$. I have also slightly clarified why the credible intervals not matching confidence intervals is bad. I, a fairly devout ...
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### Practical hyperparameter optimization: Random vs. grid search

I'm currently going through Bengio's and Bergsta's Random Search for Hyper-Parameter Optimization [1] where the authors claim random search is more efficient than grid search in achieving ...
9k views

### When are genetic algorithms a good choice for optimization?

Genetic algorithms are one form of optimization method. Often stochastic gradient descent and its derivatives are the best choice for function optimization, but genetic algorithms are still sometimes ...
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### How could stochastic gradient descent save time compared to standard gradient descent?

Standard Gradient Descent would compute gradient for the entire training dataset. ...
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### How to run linear regression in a parallel/distributed way for big data setting?

I am working on a very large linear regression problem, with data size so large that they have to be stored on a cluster of machines. It will be way too big to aggregate all the samples into one ...
552 views

### Optimization of stochastic computer models

This is a tough topic for myself to google since having the words optimization and stochastic in a search almost automatically defaults to searches for stochastic optimization. But what I really want ...
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### What are some of the disavantage of bayesian hyper parameter optimization?

I am fairly new to machine learning and statistics but I was wondering why bayesian optimization is not referred more often online when learning machine learning to optimize your algorithm ...
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### SVM Hyperparameters Tuning

I am using SVM classifier to classify data, My dataset consist of about 1 milion samples, Currently im in the stage of tunning the machine , Try to find the best parameters including a suitable ...
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### hyperparameter tuning in neural networks

I was trying to fine tune a neural network model for a multilabel classification problem. I was reading Jason Brownlee 's article for the same. As per the article, there are a number of parameters to ...
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### Markov chain Monte Carlo (MCMC) for Maximum Likelihood Estimation (MLE)

I am reading a 1991 conference paper by Geyer which is linked below. In it he seems to elude to a method that can use MCMC for MLE parameter estimation This excites me since, I have coded BFGS ...
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### Best way to evaluate PDF estimation methods

I wish to test some of my ideas that I think are better than anything that I have seen. I could be wrong but I'd like to test my ideas and vanquish my doubts by more certain observations. What I have ...
6k views

### Using genetic algorithm for hyperparameter optimization

In machine learning, I've learned one of the ways to optimize hyperparameters of a model is to do a grid search, which tests model for evenly spaced out values of hyperparametrs and determines which ...
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### Optimizing a “black box” function: Linear Regression or Bayesian Optimization… what's the difference?

Goal: I have a function $f(x,y)=z$ (two variables for illustration only) which I know almost nothing about--it has a compact domain which I can determine, it is non-negative, and bounded above. My ...
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### hyper parameter optimization grid search issues

I keep running into the same problem while doing a grid search to optimize the C and gamma parameters of an SVC. Every time i do the grid search, the best values seem to occur at around C = 100000 ...
983 views

### Faster xgb.cv for large data set

I have a record of data that contains 1.1 millions observations and 14 variables. The response is 0 or 1. It was suggested to me that I use Gradient Boosted Trees to build my logistic model. Using <...
753 views

### Bayesian Hyperparameter Tuning

I would like to implement Bayesian hyperparameter tuning in Python, but I am struggling to find a complete explanation of how it is exactly working. As anyone links, books or papers I could read about ...
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### How to optimize RBF parameters $C,\gamma$ with KSVM method?

I want to find the best choice of $C$ and $\gamma$ parameters for Radial Basis Function kernel. I am using kernlab instead of e1071 library. So how can i optimize RBF parameters $C$ and $\gamma$ with ...
468 views

### Is the AUC a robust metric to determine which network architecture to use?

I would like to figure out which hyperparameters/network architectures work best on a specific binary dataset. To compare them, I do k-fold cross-validation, use different MLP or ConvNet ...
630 views

### Regression: find the best degree of polynomial with the best regularization parameter

When trying to predict data using linear regression or classify with logistic regression, with a polynomial, I know how to find the best degree of a polynomial to fits given data when the ...
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### Best DoE method to fit Gaussian Process Regressor

I would like to fit a Gaussian Process Regressor from sk-learn to predict values of an the objective function in order to explore behaviour and interplay between inputs. Data points are difficult to ...
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### Confidence intervals and central estimates for a functional of an estimated function with uncertain parameters

I've got a problem that is leading me to dip my toes into Bayesian stats, and I've got a question about confidence (or, I suppose, credible) intervals: Say you want to know how $X$ maps to $y$. You ...
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### Machine learning algorithm to replicate a CPU-intensive, deterministic set of calculations

I am working on a data visualization like this NY Times interactive (http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html): If you go to that link, there are 20 of these charts. I ...
I have a physical model which takes $\sim50$ parameters and gives $\sim 2000$ outputs taking tens of minutes to run. I need to optimise these parameters to give outputs as close as possible to data by ...