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Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical …
Recent progress in developing general-purpose text embedders has been driven by training on synthetic LLM-generated data. Nonetheless, …
Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail …
Both PAC-Bayesian and Sample Compress learning frameworks have been shown instrumental for deriving tight (non-vacuous) generalization …
LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse …
Developing autonomous agents that can navigate diverse Graphical User Interfaces (GUIs) and solve complex tasks is essential for …
Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation …
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many …
We analyze the convergence of a novel policy gradient algorithm (referred to as SPMA) for multi-armed bandits and tabular Markov …
The rise of foundation models fine-tuned on human feedback from potentially untrusted users has increased the risk of adversarial data …