arXiv:2509.25536v1 Announce Type: cross Abstract: In this paper, we explore the asymptotically optimal tuning parameter choice in ridge regression for estimating nuisance functions of a statistical functional that has recently gained prominence in conditional independence testing and causal inference. Given…
arXiv:2506.21757v2 Announce Type: replace Abstract: Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we…
arXiv:2509.25611v1 Announce Type: cross Abstract: Transformers are deep architectures that define “in-context maps” which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a set of patches for a vision…
arXiv:2507.21807v3 Announce Type: replace Abstract: Statistical learning methods for automated variable selection, such as LASSO, elastic nets, or gradient boosting, have become increasingly popular tools for building powerful prediction models. Yet, in practice, analyses are often complicated by missing data.…
arXiv:2509.25647v1 Announce Type: cross Abstract: Branch-and-bound with preactivation splitting has been shown highly effective for deterministic verification of neural networks. In this paper, we extend this framework to the probabilistic setting. We propose BaB-prob that iteratively divides the original problem…
arXiv:1904.05877v2 Announce Type: replace-cross Abstract: Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures are…
The votes are in, and the winner of Fat Bear Week 2025 is the indomitable Bear 32, also known as Chunk. The public votes for their favorite brown bear each year in the March Madness-style tournament held by Katmai National…
Amazon’s fall hardware event had a little of everything: updated Echo speakers, several Blink security devices, and even a Kindle Scribe with a color screen. Most of the gadgets won’t ship for at least a couple of weeks, if not…
Quantization-aware training (QAT) is a leading technique for improving the accuracy of quantized neural networks. Previ- ous work has shown that decomposing training into a full-precision (FP) phase followed by a QAT phase yields superior accuracy compared to QAT alone.…
