Recent years have seen significant activity on the problem of using data...
Optimal resource allocation in modern communication networks calls for t...
Variational quantum algorithms (VQAs) offer the most promising path to
o...
A key step in quantum machine learning with classical inputs is the desi...
In vertical federated learning (FL), the features of a data sample are
d...
Meta-learning optimizes the hyperparameters of a training procedure, suc...
Machine unlearning refers to mechanisms that can remove the influence of...
The overall predictive uncertainty of a trained predictor can be decompo...
Meta-learning aims at optimizing the hyperparameters of a model class or...
The goal of these lecture notes is to review the problem of free energy
...
Meta-learning automatically infers an inductive bias by observing data f...
Meta-learning infers an inductive bias—typically in the form of the
hype...
In transfer learning, training and testing data sets are drawn from diff...
Meta-learning, or "learning to learn", refers to techniques that infer a...
Time-encoded signals, such as social network update logs and spiking tra...
We study the setting of channel coding over a family of channels whose s...
A new finite blocklength converse for the Slepian- Wolf coding problem i...