Active learning has been studied extensively as a method for efficient data col- lection. Among the many approaches in literature, Expected Error Reduction (EER) Roy & McCallum (2001) has been shown to be an effective method for ac- tive learning: select the candidate sample that, in expectation, maximally decreases the error on an unlabeled set. However, EER requires the model to be retrained for every candidate sample and thus has not been widely used for modern deep neural networks due to this large computational cost. In this paper we reformulate EER under the lens of Bayesian active learning and derive a computationally efficient version that can use any Bayesian parameter sampling method (such as Gal & Ghahramani (2016)). We then compare the empirical performance of our method using Monte Carlo dropout for parameter sampling against state of the art methods in the deep active learning literature. Experiments are performed on four standard benchmark datasets and three WILDS datasets (Koh et al., 2021).

We present a new perspective on loss minimization and the recent notion of Omniprediction through the lens of Outcome Indistingusihability. For a collection of losses and hypothesis class, omniprediction requires that a predictor provide a loss-minimization guarantee simultaneously for every loss in the collection compared to the best (loss-specific) hypothesis in the class. We present a generic template to learn predictors satisfying a guarantee we call Loss Outcome Indistinguishability. For a set of statistical tests--based on a collection of losses and hypothesis class--a predictor is Loss OI if it is indistinguishable (according to the tests) from Nature's true probabilities over outcomes. By design, Loss OI implies omniprediction in a direct and intuitive manner.

Today, we are excited to release optimizations to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, along with code to get started with deploying to Apple Silicon devices. Since its public debut in August 2022, Stable Diffusion has been adopted by a vibrant community of artists, developers and hobbyists alike, enabling the creation of unprecedented visual content with as little as a text prompt. In response, the community has built an expansive ecosystem of extensions and tools around this core technology in a matter of weeks. There are already methods that personalize Stable Diffusion, extend it to languages other than English, and more, thanks to open-source projects like Hugging Face diffusers. Beyond image generation from text prompts, developers are also discovering other creative uses for Stable Diffusion, such as image editing, in-painting, out-painting, super-resolution, style transfer and even color palette generation.