Now, if you’re like me, your first thought might be “What on earth is smoothing and why do I care?”

Before anything else what exactly are smoothed classifiers? Essentially, theyre like your regular old classifier but with a twist. Instead of making a hard prediction based on the input data, these classifiers add noise to their predictions in order to make them more robust against small perturbations or errors in the data. This can be especially useful when dealing with real-world scenarios where there might be some level of uncertainty or variability in the data.

Now, why we care about certifying this kind of classifier. The idea is that by adding noise to our predictions, were essentially creating a buffer zone around them a range within which we can be confident that the true label falls. This is where robustness certification comes in. By using mathematical techniques and algorithms, we can calculate exactly how much noise needs to be added to ensure that our classifier remains accurate even when faced with adversarial attacks or other forms of data corruption.

So why should you care about this? Well, for starters, its a major step forward in the field of AI and machine learning. By being able to certify the robustness of smoothed classifiers, we can ensure that our models are more reliable and trustworthy which is especially important when dealing with critical applications like healthcare or finance.

But let’s be real here this stuff isnt exactly easy to understand. In fact, it might make your head spin a little bit. But don’t freak out! Weve got some resources that can help you out. First off, we recommend checking out the paper “Tight certificates of adversarial robustness for randomly smoothed classifiers” by Greg Yang et al. This paper provides an in-depth look at how to calculate these certificates and why they’re important.

If thats a little too heavy for you, we also recommend checking out the paper “ANCER: Anisotropic certification via sample-wise volume maximization” by Francisco Eiras et al. This paper provides an alternative approach to robustness certification using a technique called volume maximization. It’s a bit more accessible than some of the other papers in this field, so it might be worth checking out if youre new to this topic.

Finally, we recommend taking a look at “Wasserstein smoothing: Certified robustness against Wasserstein adversarial attacks” by Alexander Levine and Soheil Feizi. This paper provides an interesting twist on the traditional approach to smoothed classifiers instead of adding noise uniformly across all dimensions, they use a technique called Wasserstein distance to add noise in a more targeted way.

Robustness certification for smoothed classifiers is a hot topic in AI and machine learning right now, and were excited to see where this field goes from here. Whether you’re a seasoned pro or just getting started with these techniques, we hope that our resources will help you understand the basics and get you on your way to creating more reliable and trustworthy models.