59 citations · 220 across the 28 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
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…
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Xubo Yue, Raed Al Kontar
Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over…