Constructing a directed cyclic graph (DCG) is challenged by both algorithmic difficulty and computational burden. Comparing multiple DCGs is even more difficult, compounded by the need to identify dynamic causalities across graphs.

Secondly, these works do not account for finite-sampling errors in the obtained curve.

Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. W atermark-ing, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating the misuse of such AI-generated content. However, we show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack--leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems, and propose guidelines and defenses for LLM watermarking in practice.

Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side

Do machines and humans process language in similar ways?

This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a

U 2 U that influences all of the variables U influences. Figure 5: Example of step one in the marginalisation, taken from Evans [22]. In this section we analyse the datasets presented in Le Quy et al. For each bias we provide a justification of our decision. Therefore we drop them from the analysis. Diabetes For this dataset, the goal is to predict if a patient will be readmitted in the next 30 days.

Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair."