Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. This raises the question of whether such networks can be trained efficiently to be worst-case robust.

A China Coast Guard vessel is seen on a giant screen showing news footage about the coast guard's law enforcement patrols in waters around Taiwan, outside a shopping mall in Beijing on April 1. | REUTERS TAIPEI - China is increasing military activities near Taiwan and honing its ability to stage a surprise attack, as well as seeking to undermine trust in the government with hybrid online warfare tactics, the island's defense ministry said on Thursday. Democratically-governed Taiwan, which China views as its own territory, has faced increased military pressure from Beijing over the past five years, including at least seven rounds of major war games around the island since 2022. China has been using artificial intelligence tools to weaken Taiwan's cybersecurity and to scan for weak points in critical infrastructure, the defense ministry said in a report released every two years. Beijing is also using hybrid warfare to weaken people's trust in the government and support for defense spending, and stepping up grey zone harassment, it added, referring to non-combat operations such as coast guard patrols designed to pressure Taiwan. Through both conventional and unconventional military actions, it aims to test its capabilities for attacking Taiwan and confronting foreign forces, the ministry said.

The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.

The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g.,

Sequential decision-making problems typically require significant human supervision and data.

Given their remarkable performance, being able to train GNNs efficiently is an important task.