Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

Grouping structures arise naturally in many statistical modeling problems. Several methods
have been proposed for variable selection that respect grouping structure in variables …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …

Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …

We give an improved theoretical analysis of score-based generative modeling. Under a
score estimate with small $ L^ 2$ error (averaged across timesteps), we provide efficient …

There is mounting evidence of emergent phenomena in the capabilities of deep learning
methods as we scale up datasets, model sizes, and training times. While there are some …

Recently, learning algorithms motivated from sharpness of loss surface as an effective
measure of generalization gap have shown state-of-the-art performances. Nevertheless …

Abstract Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated a significant performance boost …

Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …