Osvaldo Simeone

research

∙ 05/18/2023

SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal Prediction

Spiking neural networks (SNNs) process time-series data via internal eve...

0 Jiechen Chen, et al. ∙

research

∙ 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...

0 Haifeng Wen, et al. ∙

research

∙ 05/12/2023

Calibration-Aware Bayesian Learning

Deep learning models, including modern systems like large language model...

0 Jiayi Huang, et al. ∙

research

∙ 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 ...

0 Tomer Raviv, et al. ∙

research

∙ 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...

0 Boning Zhang, et al. ∙

research

∙ 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...

0 Sangwoo Park, et al. ∙

research

∙ 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)...

0 Kfir M. Cohen, et al. ∙

research

∙ 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...

0 Tomer Raviv, et al. ∙

research

∙ 02/02/2023

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Bayesian Neural Networks (BNNs) can overcome the problem of overconfiden...

0 Prabodh Katti, et al. ∙

research

∙ 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...

0 Ivana Nikoloska, et al. ∙

research

∙ 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...

0 Yunchuan Zhang, et al. ∙

research

∙ 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...

0 Meiyi Zhu, et al. ∙

research

∙ 12/15/2022

Calibrating AI Models for Wireless Communications via Conformal Prediction

When used in complex engineered systems, such as communication networks,...

0 Kfir M. Cohen, et al. ∙

research

∙ 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...

0 Hari Hara Suthan Chittoor, et al. ∙

research

∙ 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...

0 Clement Ruah, et al. ∙

research

∙ 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...

0 Clement Ruah, et al. ∙

research

∙ 10/11/2022

Network Topology Inference based on Timing Meta-Data

Consider a processor having access only to meta-data consisting of the t...

0 Wenbo Du, et al. ∙

research

∙ 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...

0 Kfir M. Cohen, et al. ∙

research

∙ 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...

0 Sangwoo Park, et al. ∙

research

∙ 09/24/2022

Neuromorphic Integrated Sensing and Communications

Neuromorphic computing is an emerging technology that support event-driv...

0 Jiechen Chen, et al. ∙

research

∙ 09/23/2022

Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis

Variational quantum algorithms (VQAs) offer the most promising path to o...

0 Sharu Theresa Jose, et al. ∙

research

∙ 09/14/2022

Compressed Particle-Based Federated Bayesian Learning and Unlearning

Conventional frequentist FL schemes are known to yield overconfident dec...

0 Jinu Gong, et al. ∙

research

∙ 08/29/2022

Bayesian Continual Learning via Spiking Neural Networks

Among the main features of biological intelligence are energy efficiency...

32 Nicolas Skatchkovsky, et al. ∙

research

∙ 07/22/2022

Learning Distributed Quantum State Discrimination with Noisy Classical Communications

Consider a distributed quantum sensing system in which Alice and Bob are...

0 Hari Hara Suthan Chittoor, et al. ∙

research

∙ 07/13/2022

Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks

Unmanned aerial base stations (UABSs) can be deployed in vehicular wirel...

0 Riccardo Marini, et al. ∙

research

∙ 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...

0 Matteo Zecchin, et al. ∙

research

∙ 06/13/2022

Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference

Neuromorphic computing is an emerging computing paradigm that moves away...

0 Jiechen Chen, et al. ∙

research

∙ 05/17/2022

Learning Quantum Entanglement Distillation with Noisy Classical Communications

Quantum networking relies on the management and exploitation of entangle...

0 Hari Hara Suthan Chittoor, et al. ∙

research

∙ 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...

0 Ivana Nikoloska, et al. ∙

research

∙ 03/27/2022

Online Meta-Learning For Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep ...

0 Tomer Raviv, et al. ∙

research

∙ 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...

0 Sangwoo Park, et al. ∙

research

∙ 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...

0 DaeSung Yu, et al. ∙

research

∙ 03/03/2022

Robust PAC^m: Training Ensemble Models Under Model Misspecification and Outliers

Standard Bayesian learning is known to have suboptimal generalization ca...

0 Matteo Zecchin, et al. ∙

research

∙ 02/28/2022

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo

For engineering applications of artificial intelligence, Bayesian learni...

0 Yunchuan Zhang, et al. ∙

research

∙ 01/21/2022

Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization

Quantum machine learning has emerged as a potential practical applicatio...

0 Ivana Nikoloska, et al. ∙

research

∙ 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...

0 Sharu Theresa Jose, et al. ∙

research

∙ 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...

8 Feng Zhu, et al. ∙

research

∙ 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...

0 Hari Hara Suthan Chittoor, et al. ∙

research

∙ 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...

0 Roy Karasik, et al. ∙

research

∙ 11/23/2021

Forget-SVGD: Particle-Based Bayesian Federated Unlearning

Variational particle-based Bayesian learning methods have the advantage ...

0 Jinu Gong, et al. ∙

research

∙ 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...

0 Bleema Rosenfeld, et al. ∙

research

∙ 10/19/2021

BAMLD: Bayesian Active Meta-Learning by Disagreement

Data-efficient learning algorithms are essential in many practical appli...

0 Ivana Nikoloska, et al. ∙

research

∙ 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...

0 Sharu Theresa Jose, et al. ∙

research

∙ 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...

0 Sangwoo Park, et al. ∙

research

∙ 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...

0 Dongzhu Liu, et al. ∙

research

∙ 08/09/2021

Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis

Objective: Functional coupling between the motor cortex and muscle activ...

0 Zhenghao Guo, et al. ∙

research

∙ 08/04/2021

Black-Box and Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

In this paper, we consider the problem of power control for a wireless n...

0 Ivana Nikoloska, et al. ∙

research

∙ 08/02/2021

Learning to Learn to Demodulate with Uncertainty Quantification via Bayesian Meta-Learning

Meta-learning, or learning to learn, offers a principled framework for f...

0 Kfir M. Cohen, et al. ∙

research

∙ 06/20/2021

Transfer Bayesian Meta-learning via Weighted Free Energy Minimization

Meta-learning optimizes the hyperparameters of a training procedure, suc...

0 Yunchuan Zhang, et al. ∙

research

∙ 06/14/2021

Latency-Constrained Prediction of mmWave/THz Link Blockages through Meta-Learning

Wireless applications that use high-reliability low-latency links depend...

0 Anders E. Kalør, et al. ∙

Osvaldo Simeone

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