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Cross-modal (image-to-text and text-to-image) retrieval is an established task used in evaluation benchmarks to test the performance of vision-language models (VLMs).

A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-V alue (KV) caching.

We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting.

Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.

They can be constructed to satisfy conditions for identifiability and to answer interventional queries, providing ground truth labels for estimates.