'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. 'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. Facing AI and a tough job market, gen Z turns to entrepreneurship: 'I have to prove myself' When Ashley Terrell graduated from the University of Hawaii in 2024, she planned to find a job in marketing, maybe for a tech company.

Contrastive learning has delivered impressive results for various tasks in the selfsupervised regime.

We propose a general method to construct centroid approximation for the distribution of maximum points of a random function (a.k.a.

In this section, we provide and expand upon a toy example. Recall that the inputs x and x0 need not correspond to real users but could instead represent hypothetical users. Example 5. Suppose that the regulatory guideline requires that users in the same geographical location receive similar weather forecasts. This can be written as "the weather forecasts that are selected by F should be similar for all users in the same geographical location", and S could be a randomly generated set of user pairs, where each pair corresponds to two (hypothetical) users in the same geographical location, and S could contain pairs across many locations. In the left-most panel, a filtering algorithm F takes in counterfactual inputs x and x0 and produces the content Z and Z0. Because a counterfactual regulation requires that F behave similarly under x and x0, the regulation is effectively requiring that content Z and Z0 are sufficiently similar (or, graphically, that they are close in Z). The question of how to quantify "similarity" is addressed in Section 2.1. The toy example in Example 5 is illustrated in the right-most panel.

By filtering the content that users see, social media platforms have the ability to influence users' perceptions and decisions, from their dining choices to their voting preferences. This influence has drawn scrutiny, with many calling for regulations on filtering algorithms, but designing and enforcing regulations remains challenging. In this work, we examine three questions. First, given a regulation, how would one design an audit to enforce it? Second, does the audit impose a performance cost on the platform?

Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis. The few existing methods often require unrealistic setups, such as access to internal information of the victim models, or an impractically-large number of queries. We present a novel Bayesian optimisation-based attack method for graph classification models. Our method is black-box, query-efficient and parsimonious with respect to the perturbation applied. We empirically validate the effectiveness and flexibility of the proposed method on a wide range of graph classification tasks involving varying graph properties, constraints and modes of attack. Finally, we analyse common interpretable patterns behind the adversarial samples produced, which may shed further light on the adversarial robustness of graph classification models.

A.1 Additional lemma Lemma 9 Let s0 be the starting state, let (a)n represent a sequence of actions and let M = Z(ar)Z(ar 1)...Z(a1) i.e., the product of matrices in {Z(a)}left multiplied in order of the sequence Proof Here we use proof by induction. We note that the interchange of the integral and infinite summation is justified by Section 3.7 in [5], since the coefficients Z We can then conclude the statement of the lemma by induction. A.2 Proof of Proposition 1 Proof By Lemma 9, given a fixed sequence of actions (a)n, the r-th state sr under this sequence of actions starting from state s0 has a distribution that can be represented over the basis {φn(s)}. Therefore, the expected reward under any sequence of actions for reward Ris the same as for the projected reward R0 for any state sr where r > 0. The reward at the starting state, R(s0) does not depend on the policy. Therefore, the value of R(s0) does not change whether a policy is optimal or not.