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09/20/2023
Statistical Complexity of Quantum Learning
Recent years have seen significant activity on the problem of using data...
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08/22/2023
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized b...
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08/08/2023
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Consider a setting in which devices and a server share a pre-trained mod...
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06/30/2023
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction
Black-box zero-th order optimization is a central primitive for applicat...
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05/18/2023
SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal Prediction
Spiking neural networks (SNNs) process time-series data via internal eve...
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05/18/2023
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping
One of the key challenges towards the deployment of over-the-air federat...
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05/12/2023
Calibration-Aware Bayesian Learning
Deep learning models, including modern systems like large language model...
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05/12/2023
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Artificial intelligence (AI) is envisioned to play a key role in future ...
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05/07/2023
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
The recent development of scalable Bayesian inference methods has renewe...
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04/06/2023
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
Quantum machine learning is a promising programming paradigm for the opt...
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02/15/2023
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC)...
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02/05/2023
Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers
In the design of wireless receivers, DNNs can be combined with tradition...
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02/02/2023
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Bayesian Neural Networks (BNNs) can overcome the problem of overconfiden...
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01/19/2023
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
Adaptive gating plays a key role in temporal data processing via classic...
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01/16/2023
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Optimal resource allocation in modern communication networks calls for t...
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12/19/2022
Semantics-Aware Remote Estimation via Information Bottleneck-Inspired Type Based Multiple Access
Type-based multiple access (TBMA) is a semantics-aware multiple access p...
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12/15/2022
Calibrating AI Models for Wireless Communications via Conformal Prediction
When used in complex engineered systems, such as communication networks,...
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12/09/2022
Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
Simulating quantum channels is a fundamental primitive in quantum comput...
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12/02/2022
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems
Commonly adopted in the manufacturing and aerospace sectors, digital twi...
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10/11/2022
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
Commonly adopted in the manufacturing and aerospace sectors, digital twi...
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10/11/2022
Network Topology Inference based on Timing Meta-Data
Consider a processor having access only to meta-data consisting of the t...
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10/10/2022
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
AI tools can be useful to address model deficits in the design of commun...
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10/06/2022
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
Conventional frequentist learning is known to yield poorly calibrated mo...
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09/24/2022
Neuromorphic Integrated Sensing and Communications
Neuromorphic computing is an emerging technology that support event-driv...
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09/23/2022
Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis
Variational quantum algorithms (VQAs) offer the most promising path to o...
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09/14/2022
Compressed Particle-Based Federated Bayesian Learning and Unlearning
Conventional frequentist FL schemes are known to yield overconfident dec...
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08/29/2022
Bayesian Continual Learning via Spiking Neural Networks
Among the main features of biological intelligence are energy efficiency...
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07/22/2022
Learning Distributed Quantum State Discrimination with Noisy Classical Communications
Consider a distributed quantum sensing system in which Alice and Bob are...
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07/13/2022
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
Unmanned aerial base stations (UABSs) can be deployed in vehicular wirel...
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07/01/2022
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
This work takes a critical look at the application of conventional machi...
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06/13/2022
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
Neuromorphic computing is an emerging computing paradigm that moves away...
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05/17/2022
Learning Quantum Entanglement Distillation with Noisy Classical Communications
Quantum networking relies on the management and exploitation of entangle...
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03/31/2022
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines
Near-term noisy intermediate-scale quantum circuits can efficiently impl...
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03/27/2022
Online Meta-Learning For Hybrid Model-Based Deep Receivers
Recent years have witnessed growing interest in the application of deep ...
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03/23/2022
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
An efficient data-driven prediction strategy for multi-antenna frequency...
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03/07/2022
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for Cell-Free MIMO Systems
Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates b...
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03/03/2022
Robust PAC^m: Training Ensemble Models Under Model Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization ca...
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02/28/2022
Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo
For engineering applications of artificial intelligence, Bayesian learni...
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01/21/2022
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
Quantum machine learning has emerged as a potential practical applicatio...
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01/17/2022
Transfer Learning in Quantum Parametric Classifiers: An Information-Theoretic Generalization Analysis
A key step in quantum machine learning with classical inputs is the desi...
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01/12/2022
Adaptive Worker Grouping For Communication-Efficient and Straggler-Tolerant Distributed SGD
Wall-clock convergence time and communication load are key performance m...
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12/17/2021
Coded Consensus Monte Carlo: Robust One-Shot Distributed Bayesian Learning with Stragglers
This letter studies distributed Bayesian learning in a setting encompass...
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12/03/2021
Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk
Broadcast/multicast communication systems are typically designed to opti...
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11/23/2021
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Variational particle-based Bayesian learning methods have the advantage ...
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11/02/2021
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Neuromorphic data carries information in spatio-temporal patterns encode...
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10/19/2021
BAMLD: Bayesian Active Meta-Learning by Disagreement
Data-efficient learning algorithms are essential in many practical appli...
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10/11/2021
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints
In vertical federated learning (FL), the features of a data sample are d...
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10/01/2021
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
Predicting fading channels is a classical problem with a vast array of a...
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08/17/2021
Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy
Most works on federated learning (FL) focus on the most common frequenti...
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08/09/2021
