Search Results for author:
Found 19 papers, 4 papers with code
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of processes yielding high rewards, and the exploration of uncertain processes.
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes
Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
In this work, we study the problem of common and unique feature extraction from noisy data.
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples.
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets.
Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design
Empirically, through simulated datasets and a real-world collaborative sensor design experiment, we show that our framework can effectively accelerate and improve the optimal design process and benefit all participants.
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
This necessitates rethinking cost-sensitive classification in DNNs.
Personalized PCA: Decoupling Shared and Unique Features
In this paper, we tackle a significant challenge in PCA: heterogeneity.
Federated Data Analytics: A Study on Linear Models
In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression.
A Continual Learning Framework for Adaptive Defect Classification and Inspection
Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes.
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations.
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage.
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings.
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL).
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage.
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
SALR: Sharpness-aware Learning Rates for Improved Generalization
In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes
A fundamental assumption is that the output/group membership labels for all observations are known.
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
We then provide both a theoretical and practical guideline to decide on the rolling horizon stagewise.
