In image classification, it is common to utilize a pretrained model to extract meaningful features of the input images, and then to train a classifier on top of it to make predictions for any downstream task.

Multi-distribution or collaborative learning involves learning a single predictor that works well across multiple data distributions, using samples from each during training.

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances.

We give a sufficient criterion to ensure that the problem is well-conditioned.

The tech bros might show more humility in Delhi - but will they make AI any safer? Those who shout the loudest about artificial intelligence tend to be in the West, notably the US and Europe. So it's significant that a gathering of powerful leaders is being held in the Global South, a region of the world that runs the risk of being left behind in the AI race. Tech bosses, politicians, scientists, academics and campaigners are meeting at the AI Impact Summit in India this week for top-level discussions about what the world should be doing to try to marshal the AI revolution in the right direction. At last year's AI Action Summit, as it was then known, an ugly power struggle broke out between some Western countries over who should be in charge.

These profiles are fed back to the LLM, which then revises the code to reduce overhead.