Search Results for author:
Found 33 papers, 13 papers with code
Anomaly Detection in Power Grids via Context-Agnostic Learning
Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the network topology or measurement data?
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs.
Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate.
Cross-Validation Conformal Risk Control
CV-CRC is proved to offer theoretical guarantees on the average risk of the set predictor.
Generalization and Informativeness of Conformal Prediction
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification.
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy.
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction
Focusing on methods based on Bayesian optimization (BO), prior art has introduced an optimization scheme -- referred to as SAFEOPT -- that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met.
SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal Prediction
Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation.
Calibration-Aware Bayesian Learning
Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions.
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees.
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary.
Calibrating AI Models for Wireless Communications via Conformal Prediction
This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
Fast-Convergent Federated Learning via Cyclic Aggregation
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server.
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial edge devices.
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution.
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions.
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
In this context, we explore the application of the framework of robust Bayesian learning.
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-selective channels must operate based on a small number of pilot symbols.
Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction.
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization
Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
Under a Bayesian formulation, assuming a well-specified model, the two contributions can be exactly expressed (for the log-loss) or bounded (for more general losses) in terms of information-theoretic quantities (Xu and Raginsky, 2020).
Balance-Oriented Focal Loss with Linear Scheduling for Anchor Free Object Detection
This work aims to address imbalance problem in the situation of using a general unbalanced data of non-extreme distribution not including few shot and the focal loss for anchor free object detector.
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
The proposed approach is based on a meta-training phase in which the online gradient-based meta-learning of the decoder is coupled with the joint training of the encoder via the transmission of pilots and the use of a feedback link.
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations.
Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework
The proposed framework consists of four models: spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection.
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder.
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel.
Learning How to Demodulate from Few Pilots via Meta-Learning
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols.
