Madry et al. provide an interpretation of training on adversarial examples as sattle-point (i.e. min-max) problem. Based on this formulation, they conduct several experiments on MNIST and CIFAR-10 supporting the following conclusions:
- Projected gradient descent might be “strongest” adversary using first-order information. Here, gradient descent is used to maximize the loss of the classifier directly while always projecting onto the set of “allowed” perturbations (e.g. within an $\epsilon$-ball around the samples). This observation is based on a large number of random restarts used for projected gradient descent. Regarding the number of restarts, the authors also note that an adversary should be bounded regarding the computation resources – similar to polynomially bounded adversaries in cryptography.
- Network capacity plays an important role in training robust neural networks using the min-max formulation (i.e. using adversarial training). In particular, the authors suggest that increased capacity is needed to fit/learn adversarial examples without overfitting. Additionally, increased capacity (in combination with a strong adversary) decreases transferability of adversarial examples.

Also view this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

In this tutorial paper, Carl E. Rasmussen gives an introduction to Gaussian Process Regression focusing on the definition, the hyperparameter learning and future research directions.

A Gaussian Process is completely defined by its mean function $m(\pmb{x})$ and its covariance function (kernel) $k(\pmb{x},\pmb{x}')$. The mean function $m(\pmb{x})$ corresponds to the mean vector $\pmb{\mu}$ of a Gaussian distribution whereas the covariance function $k(\pmb{x}, \pmb{x}')$ corresponds to the covariance matrix $\pmb{\Sigma}$. Thus, a Gaussian Process $f \sim \mathcal{GP}\left(m(\pmb{x}), k(\pmb{x}, \pmb{x}')\right)$ is a generalization of a Gaussian distribution over vectors to a distribution over functions. A random function vector $\pmb{\mathrm{f}}$ can be generated by a Gaussian Process through the following procedure:

1. Compute the components $\mu_i$ of the mean vector $\pmb{\mu}$ for each input $\pmb{x}_i$ using the mean function $m(\pmb{x})$
2. Compute the components $\Sigma_{ij}$ of the covariance matrix $\pmb{\Sigma}$ using the covariance function $k(\pmb{x}, \pmb{x}')$
3. A function vector $\pmb{\mathrm{f}} = [f(\pmb{x}_1), \dots, f(\pmb{x}_n)]^T$ can be drawn from the Gaussian distribution $\pmb{\mathrm{f}} \sim \mathcal{N}\left(\pmb{\mu}, \pmb{\Sigma} \right)$

Applying this procedure to regression, means that the resulting function vector $\pmb{\mathrm{f}}$ shall be drawn in a way that a function vector $\pmb{\mathrm{f}}$ is rejected if it does not comply with the training data $\mathcal{D}$. This is achieved by conditioning the distribution on the training data $\mathcal{D}$ yielding the posterior Gaussian Process $f \rvert \mathcal{D} \sim \mathcal{GP}(m_D(\pmb{x}), k_D(\pmb{x},\pmb{x}'))$ for noise-free observations with the posterior mean function $m_D(\pmb{x}) = m(\pmb{x}) + \pmb{\Sigma}(\pmb{X},\pmb{x})^T \pmb{\Sigma}^{-1}(\pmb{\mathrm{f}} - \pmb{\mathrm{m}})$  and the posterior covariance function $k_D(\pmb{x},\pmb{x}')=k(\pmb{x},\pmb{x}') - \pmb{\Sigma}(\pmb{X}, \pmb{x}')$ with $\pmb{\Sigma}(\pmb{X},\pmb{x})$ being a vector of covariances between every training case of $\pmb{X}$ and $\pmb{x}$.

Noisy observations $y(\pmb{x}) = f(\pmb{x}) + \epsilon$ with $\epsilon \sim \mathcal{N}(0,\sigma_n^2)$ can be taken into account with a second Gaussian Process with mean $m$ and covariance function $k$ resulting in $f \sim \mathcal{GP}(m,k)$ and $y \sim \mathcal{GP}(m, k + \sigma_n^2\delta_{ii'})$. The figure illustrates the cases of noisy observations (variance at training points) and of noise-free observationshttps://i.imgur.com/BWvsB7T.png (no variance at training points).

In the Machine Learning perspective, the mean and the covariance function are parametrised by hyperparameters and provide thus a way to include prior knowledge e.g. knowing that the mean function is a second order polynomial. To find the optimal hyperparameters $\pmb{\theta}$, 

1. determine the log marginal likelihood $L= \mathrm{log}(p(\pmb{y} \rvert \pmb{x}, \pmb{\theta}))$,
2. take the first partial derivatives of $L$ w.r.t. the hyperparameters, and
3. apply an optimization algorithm.

It should be noted that a regularization term is not necessary for the log marginal likelihood $L$ because it already contains a complexity penalty term. Also, the tradeoff between data-fit and penalty is performed automatically.

Gaussian Processes provide a very flexible way for finding a suitable regression model. However, they require the high computational complexity $\mathcal{O}(n^3)$ due to the inversion of the covariance matrix. In addition, the generalization of Gaussian Processes to non-Gaussian likelihoods remains complicated. 

Hein and Andriushchenko give a intuitive bound on the robustness of neural networks based on the local Lipschitz constant. With robustness, the authors refer a small $\epsilon$-ball around each sample; this ball is supposed to describe the region where the neural network predicts a constant class. This means that adversarial examples have to compute changes large enough to leave these robust areas. Larger $\epsilon$-balls imply higher robustness to adversarial examples.

When considering a single example $x$, and a classifier $f = (f_1, \ldots, f_K)^T$ (i.e. in a multi-class setting), the bound can be stated as follows. For $q$ and $p$ such that $\frac{1}{q} + \frac{1}{p} = 1$ and $c$ being the class predicted for $x$, the it holds

$x = \arg\max_j f_j(x + \delta)$

for all $\delta$ with

$\|\delta\|_p \leq \max_{R > 0}\min \left\{\min_{j \neq c} \frac{f_c(x) – f_j(x)}{\max_{y \in B_p(x, R)} \|\nabla f_c(y) - \nabla f_j(y)\|_q}, R\right\}$.

Here, $B_p(x, R)$ describes the $R$-ball around $x$ measured using the $p$-norm. Based on the local Lipschitz constant (in the denominator), the bound essentially measures how far we can deviate from the sample $x$ (measured in the $p$-norm) until $f_j(x) > f_c(x)$ for some $j \neq c$. The higher the local Lipschitz constant, the smaller deviations are allowed, i.e. adversarial examples are easier to find. Note that the bound also depends on the confidence, i.e. the edge  $f_c(x)$ has in comparison to all other $f_j(x)$.

In the remaining paper, the authors also provide bounds for simple classifiers including linear classifiers, kernel methods and two-layer perceptrons (i.e. one hidden layer). For the latter, they also propose a new type of regularization called cross-Lipschitz regularization:

$P(f) = \frac{1}{nK^2} \sum_{i = 1}^n \sum_{l,m = 1}^K \|\nabla f_l(x_i) - \nabla f_m(x_i)\|_2^2$.

This regularization term is intended to reduce the Lipschitz constant locally around training examples. They show experimental results using this regularization on MNIST and CIFAR, see the paper for details.

Also view this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Although Machine learning models have been accepted widely as the next step towards simplifying complex problems, the inner workings of a machine learning model are still unclear and these details can lead to an increase in trust of the model prediction, and the model itself. 

**Idea: ** A good explanation system that can justify the prediction of a classifier and can lead to diagnosing the reasoning behind a model can exponentially raise one’s trust in the predictive model.

**Solution: ** This paper proposes a local explanation model called LIME, that approximates a linear local explanation with respect to a data point. The paper outlines desired characteristics for explainers and expounds on how LIME matches to these characteristics, the characteristics being 1) Interpretable 2) Local Fidelity 3) Model-Agnostic and 4) Provides a global perspective. This paper also explores the concept of Fidelity-Interpretability Trade-off; The more complex a model is the less interpretable a completely faithful explanation would be, thus a balance needs to be struck between interpretability and fidelity for complex models. The paper outlines in detail how the proposed LIME explanation model works, for different types of predictive classifiers. LIME works by generating random data points around a test data point and approximating a linear explanation for these randomized points. Thus, LIME works on a rather large assumption that every complex model is linear on a microscopic level. This assumption although large seems justified for most models, although this could lead to certain global issues when analyzing a complex model on the whole.

This is a mildly silly paper to summarize, since there isn't really a new mechanism to understand, but rather a number of straightforward (and interesting!) empirical results that are also quite well-explained in the paper itself. That said, for the sake of a tiny bit more brevity than the paper itself provides, I'll try to pull out some of the conclusions I found the most interesting here. 

The general goal of this paper is to better understand the contours of when self-supervised representation learning is valuable for vision (and specifically when it can compete with supervised learning), and when it doesn't. In general, the results are all using ResNet backbones, with SimCLR SSL, on image classification datasets. Some bullet-point takeaways: 

- The SSL models being tested here seem to roughly saturate at unsupervised dataset sizes of around 500K; the comparative jump from dataset sizes of 500K to 1M is fairly small.
- Once you have a supervised dataset of around 50K or more, the benefit of SSL pretraining starts to diminish, and it converges to being more similar to just supervised learning on that numbrer of labeled images. On the flip side, it's only possible to get close to "good" fully supervised performance by using 100K images or more on top of a SSL baseline.
- Even within image classification datasets, it's much better to do SSL representation on the same dataset as the one you'll use for downstream training; trying to transfer representations to different datasets leads to meaningfully worse results. Interestingly, this is even true when you add out-of-domain (i.e. other-dataset) data to an existing in-domain dataset: a dataset of 250K in-dataset images does better than a 500K dataset of images from mixed datasets, and does notably better than a 1M dataset of mixed images. In this case, adding more out-of-domain images seems to have just degraded performance
- SSL seems to perform more closely to SL on a course label set; when the label set gets more granular, the task gets harder overall, but, more specifically, the gap between SSL and SL grows
- When the authors tried different forms of dataset corruption, SSL was much more robust to adding salt-and-pepper noise than it was to removing high-frequency information in the form of reducing the images to a lower resolution.