Security event correlation approaches are necessary to detect and predict incremental
threats such as multi-step or targeted attacks (advanced persistent threats) and other causal …

Learning individual-level causal effects from observational data, such as inferring the most
effective medication for a specific patient, is a problem of growing importance for policy …

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing
attention to the potential impacts of AI technologies on society. In this paper we discuss one …

Self-supervised learning is an empirically successful approach to unsupervised learning
based on creating artificial supervised learning problems. A popular self-supervised …

We study high-dimensional distribution learning in an agnostic setting where an adversary is
allowed to arbitrarily corrupt an ε-fraction of the samples. Such questions have a rich history …

Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous
experience to rapidly learn new skills or adapt to new environments. Representation …

This work considers a computationally and statistically efficient parameter estimation method
for a wide class of latent variable models—including Gaussian mixture models, hidden …

We propose Regularized Learning under Label shifts (RLLS), a principled and a practical
domain-adaptation algorithm to correct for shifts in the label distribution between a source …

Abstract The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a
router, has achieved great success in deep learning. However, the understanding of such …

Tensors are multidimensional arrays of numerical values and therefore generalize matrices
to multiple dimensions. While tensors first emerged in the psychometrics community in the …