Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of computationally efficient data-driven algorithms for combinatorial algorithm families with multiple parameters.

Google's AI Overviews only issue a warning if users choose to request additional health information, by selecting'Show more'. Google's AI Overviews only issue a warning if users choose to request additional health information, by selecting'Show more'. Google is putting people at risk of harm by downplaying safety warnings that its AI-generated medical advice may be wrong. When answering queries about sensitive topics such as health, the company says its AI Overviews, which appear above search results, prompt users to seek professional help, rather than relying solely on its summaries. "AI Overviews will inform people when it's important to seek out expert advice or to verify the information presented," Google has said .

Recently, Chiang et al. (2023) showed that finite-precision transformer classifiers

However, existing approaches to DTs often struggle to generalize to unseen conditions in data-scarce settings, a crucial requirement for such models. To address these limitations, our work begins by establishing the essential desiderata for effective DTs.

Large language models(LLMs) have sparked a new wave of exciting AI applications. Hosting these models at scale requires significant memory resources.

Such a lack of alignment and uniformity might restrict the transferability and robustness of embeddings. To this end, we devise a new fine-tuning method for robust representation equipping better alignment and uniformity. First, we propose a Geodesic Multi-Modal Mixup that mixes the embeddings of image and text to generate hard negative samples on the hypersphere.

Figure 1: Evaluation of T abEBM and other state-of-the-art tabular generative methods across six key metrics (larger area indicates better performance).