59 citations · 220 across the 28 of their papers we have counts for
30 papers
Language-Induced Priors for Domain Adaptation
Qiyuan Chen, Jiayu Zhou, Raed Al Kontar
Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant source domains from irrelevant…
Collaborative Contextual Bayesian Optimization
Chih-Yu Chang, Qiyuan Chen, Tianhan Gao +5
Discovering optimal designs through sequential data collection is essential in many real-world applications. While Bayesian Optimization (BO) has achieved remarkable success in thi…
Online Learning of Optimal Sequential Testing Policies
Qiyuan Chen, Raed Al Kontar
This paper studies an online learning problem that seeks optimal testing policies for a stream of subjects, each of whom can be evaluated through a sequence of candidate tests draw…
ALBATROSS: Cheap Filtration Based Geometry via Stochastic Sub-Sampling
Andrew J. Stier, Naichen Shi, Raed Al Kontar +2
Topological data analysis (TDA) detects geometric structure in biological data. However, many TDA algorithms are memory intensive and impractical for massive datasets. Here, we int…
A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printing
Weishi Wang, Sicong Guo, Chenhuan Jiang +5
Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D pr…
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Haoyi Song, Ruihan Ji, Naichen Shi +2
Large language models (LLMs) have transformed natural language processing, but their reliable deployment requires effective uncertainty quantification (UQ). Existing UQ methods are…
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Chih-Yu Chang, Milad Azvar, Chinedum Okwudire +1
Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkab…
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective
Raed Al Kontar
We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentati…
The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
Qiyuan Chen, Raed Al Kontar
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur…
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Tanapol Kosolwattana, Huazheng Wang, Raed Al Kontar +1
Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of p…
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
Naichen Shi, Salar Fattahi, Raed Al Kontar
In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by…
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes
Seokhyun Chung, Raed Al Kontar
Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer…
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Xiaoyang Song, Wenbo Sun, Maher Nouiehed +2
Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase…
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
Jiuyun Hu, Naichen Shi, Raed Al Kontar +1
We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets…
Personalized Dictionary Learning for Heterogeneous Datasets
Geyu Liang, Naichen Shi, Raed Al Kontar +1
We introduce a relevant yet challenging problem named Personalized Dictionary Learning (PerDL), where the goal is to learn sparse linear representations from heterogeneous datasets…
Heterogeneous Matrix Factorization: When Features Differ by Datasets
Naichen Shi, Raed Al Kontar, Salar Fattahi
In myriad statistical applications, data are collected from related but heterogeneous sources. These sources share some commonalities while containing idiosyncratic characteristics…
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
Qiyuan Chen, Raed Al Kontar, Maher Nouiehed +2
Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cos…
Personalized PCA: Decoupling Shared and Unique Features
Naichen Shi, Raed Al Kontar
In this paper, we tackle a significant challenge in PCA: heterogeneity. When data are collected from different sources with heterogeneous trends while still sharing some congruency…
Federated Data Analytics: A Study on Linear Models
Xubo Yue, Raed Al Kontar, Ana María Estrada Gómez
As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process…
A Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun, Raed Al Kontar, Judy Jin +1
Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes. This article describes a general framewo…
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
Xubo Yue, Raed Al Kontar
In this paper, we propose \texttt{FGPR}: a Federated Gaussian process () regression framework that uses an averaging strategy for model aggregation and stochastic gra…
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen, Lili Zheng, Raed Al Kontar +1
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 the…
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
Raed Kontar, Naichen Shi, Xubo Yue +12
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is br…
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue, Maher Nouiehed, Raed Al Kontar
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 desi…