59 citations · 117 across the 11 of their papers we have counts for
12 papers
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…
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…
Diffusion-Based Surrogate Modeling and Multi-Fidelity Calibration
Naichen Shi, Hao Yan, Shenghan Guo +1
Physics simulations have become fundamental tools to study myriad engineering systems. As physics simulations often involve simplifications, their outputs should be calibrated usin…
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…
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…
Adam Can Converge Without Any Modification On Update Rules
Yushun Zhang, Congliang Chen, Naichen Shi +2
Ever since Reddi et al. 2018 pointed out the divergence issue of Adam, many new variants have been designed to obtain convergence. However, vanilla Adam remains exceptionally popul…
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…
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
Naichen Shi, Fan Lai, Raed Al Kontar +1
In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL). Instead of aggregating localmodels to update a single global model,…
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning
Naichen Shi, Ruichen Li, Sun Youran
People have made remarkable progress in game AIs, especially in domain of perfect information game. However, trick-taking poker game, as a popular form of imperfect information gam…