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Vision and language models that can accurately understand both images and text are crucial for deeper document understanding. These …
Training large language models (LLMs) for pretraining or adapting to new tasks and domains has become increasingly critical as their …
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks …
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners …
We analyze the convergence of a novel policy gradient algorithm (referred to as SPMA) for multi-armed bandits and tabular Markov …
Recent advancements in large language models (LLMs) have sparked interest in developing autonomous web agents capable of performing …
The ability to predict outcomes of interactions between embodied agents and objects is paramount in the robotic setting. While …
Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small …
The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the …
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially …