text: 234,61,1395182893102624772,2377407248,Daniel Whiteson,"New paper with Aishik Ghosh and @BPNachman about how networks learn when your training samples have uncertainties: <LINK> What if you are learning the wrong thing? It’s a big deal. What if your data is controlled by some unknown nuisance parameter. You might train on simulated data like this (dots) and learn a NN (shading): <LINK> But what if the real data are actually like this? <LINK> Then you have the wrong NN. You want this one: <LINK> Recently many people approach this by trying to make your NN insensitive to the unknown parameter. That doesn’t work here. There’s no network that works for all rotations. In our paper, we use a parameterized network, where the network output is a function of the unknown parameter z. Then we can treat it statistically and profile over it. <LINK> This gives the optimal performance! <LINK> @kratsg At training, you give it examples with true values of z.  At inference, you give it data and get a value as a function of z. @kratsg Technically you just treat z as an additional input, so to evaluate the NN, you need to specify z. I'll find @Aishik_Ghosh_'s code and post a link. @HEPfeickert @kratsg @Aishik_Ghosh_ Absolutely.",https://arxiv.org/abs/2105.08742,"Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced sensitivity to systematic uncertainties. Contrary to the traditional wisdom of constructing an analysis strategy that is invariant to systematic uncertainties, we study the use of a classifier that is fully aware of uncertainties and their corresponding nuisance parameters. We show that this dependence can actually enhance the sensitivity to parameters of interest. Studies are performed using a synthetic Gaussian dataset as well as a more realistic HEP dataset based on Higgs boson decays to tau leptons. For both cases, we show that the uncertainty aware approach can achieve a better sensitivity than alternative machine learning strategies. ",Uncertainty Aware Learning for High Energy Physics,10,"['New paper with Aishik Ghosh and @BPNachman about how networks learn when your training samples have uncertainties:\n\n<LINK>\n\nWhat if you are learning the wrong thing? It’s a big deal.', 'What if your data is controlled by some unknown nuisance parameter. You might train on simulated data like this (dots) and learn a NN (shading): https://t.co/ha16BptgyF', 'But what if the real data are actually like this? https://t.co/tEeJILWY02', 'Then you have the wrong NN. You want this one: https://t.co/DBfrHtwDAy', 'Recently many people approach this by trying to make your NN insensitive to the unknown parameter. That doesn’t work here.  There’s no network that works for all rotations.', 'In our paper, we use a parameterized network, where the network output is a function of the unknown parameter z.  Then we can treat it statistically and profile over it. https://t.co/K6qR0uPsN8', 'This gives the optimal performance! https://t.co/ZrATUCaTaL', '@kratsg At training, you give it examples with true values of z.  \n\nAt inference, you give it data and get a value as a function of z.', ""@kratsg Technically you just treat z as an additional input, so to evaluate the NN, you need to specify z.  I'll find @Aishik_Ghosh_'s code and post a link."", '@HEPfeickert @kratsg @Aishik_Ghosh_ Absolutely.']",21,05,1171