Conditional validity and length efficiency are two crucial aspects of conformal prediction
(CP). Achieving conditional validity ensures accurate uncertainty quantification for data …

Inexact Markov chain Monte Carlo methods rely on Markov chains that do not exactly
preserve the target distribution. Examples include the unadjusted Langevin algorithm (ULA) …

Spiking neural networks (SNNs) process time-series data via internal event-driven neural
dynamics. The energy consumption of an SNN depends on the number of spikes exchanged …

In this paper, we consider a wireless federated inference scenario in which devices and a
server share a pre-trained machine learning model. The devices communicate statistical …

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a
conventional point predictor to provide calibration guarantees. Generalizing conformal …

In modern wireless network architectures, such as Open Radio Access Network (O-RAN),
the operation of the radio access network (RAN) is managed by applications, or apps for …

The dynamic scheduling of ultra-reliable and low-latency communication traffic (URLLC) in
the uplink can significantly enhance the efficiency of coexisting services, such as enhanced …

Neuromorphic computing leverages the sparsity of temporal data to reduce processing
energy by activating a small subset of neurons and synapses at each time step. When …

In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by
typical machine learning models to determine whether a specific data point was used to train …

In recent years, the application of neural networks (NNs) in wireless communication has
garnered widespread attention and proven successful. However, conventional learning …