Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

In this paper, we make the key delineation on the roles of resolution and statistical
uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more …

In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

It is common practice in deep learning to represent a measurement of the world on a
discrete grid, eg a 2D grid of pixels. However, the underlying signal represented by these …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow
the explicit specification of prior knowledge and accurately capture model uncertainty. We …

Learning the distribution of a continuous or categorical response variable y given its
covariates x is a fundamental problem in statistics and machine learning. Deep neural …

Gradient-based meta-learning techniques are both widely applicable and proficient at
solving challenging few-shot learning and fast adaptation problems. However, they have …