no code implementations • 13 Feb 2024 • Quanjun Lang, Xiong Wang, Fei Lu, Mauro Maggioni
Modeling multi-agent systems on networks is a fundamental challenge in a wide variety of disciplines.
no code implementations • 28 Nov 2023 • Xiong Wang, Inbar Seroussi, Fei Lu
Our tLSE method offers a straightforward approach for establishing the optimal minimax rate for models with either local or nonlocal dependency.
no code implementations • 18 May 2023 • Quanjun Lang, Fei Lu
We establish a small noise analysis framework to assess the effects of norms in Tikhonov and RKHS regularizations, in the context of ill-posed linear inverse problems with Gaussian noise.
1 code implementation • 30 Jan 2023 • Zehong Zhang, Fei Lu, Esther Xu Fei, Terry Lyons, Yannis Kevrekidis, Tom Woolf
Statistical optimality benchmarking is crucial for analyzing and designing time series classification (TSC) algorithms.
no code implementations • 29 Dec 2022 • Neil K. Chada, Quanjun Lang, Fei Lu, Xiong Wang
However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator.
no code implementations • 12 Jul 2022 • Qingci An, Yannis Kevrekidis, Fei Lu, Mauro Maggioni
Assuming abundant data of the observation process along with the distribution of the state process, we introduce a nonparametric generalized moment method to estimate the observation function via constrained regression.
no code implementations • 23 May 2022 • Fei Lu, Qingci An, Yue Yu
In this work, we provide a rigorous identifiability analysis and convergence study for the learning of kernels in nonlocal operators.
no code implementations • 8 Mar 2022 • Fei Lu, Quanjun Lang, Qingci An
We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators.
no code implementations • 10 Jun 2021 • Quanjun Lang, Fei Lu
This study examines the identifiability of interaction kernels in mean-field equations of interacting particles or agents, an area of growing interest across various scientific and engineering fields.
no code implementations • 12 Apr 2021 • Fei Lu, Hyeonwoo Yu, Jean Oh
The advent of deep learning has brought an impressive advance to monocular depth estimation, e. g., supervised monocular depth estimation has been thoroughly investigated.
no code implementations • 25 Feb 2021 • Xingjie Li, Fei Lu, Felix X. -F. Ye
However, locally Lipschitz SDEs often require special treatments such as implicit schemes with small time-steps to accurately simulate the ergodic measure.
no code implementations • 20 Nov 2020 • Zhongyang Li, Fei Lu
In the learning of systems of interacting particles or agents, coercivity condition ensures identifiability of the interaction functions, providing the foundation of learning by nonparametric regression.
no code implementations • 29 Oct 2020 • Quanjun Lang, Fei Lu
We introduce a nonparametric algorithm to learn interaction kernels of mean-field equations for 1st-order systems of interacting particles.
no code implementations • 30 Jul 2020 • Fei Lu, Mauro Maggioni, Sui Tang
Finally, we exhibit an efficient parallel algorithm to construct the estimator from data, and we demonstrate the effectiveness of our algorithm with numerical tests on prototype systems including stochastic opinion dynamics and a Lennard-Jones model.
no code implementations • 10 Oct 2019 • Fei Lu, Mauro Maggioni, Sui Tang
These simulations also suggest that our estimators are robust to noise in the observations, and produce accurate predictions of dynamics in relative large time intervals, even when they are learned from data collected in short time intervals.
no code implementations • 21 Aug 2019 • Kevin K. Lin, Fei Lu
Model reduction methods aim to describe complex dynamic phenomena using only relevant dynamical variables, decreasing computational cost, and potentially highlighting key dynamical mechanisms.
no code implementations • 11 Mar 2019 • Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
1 code implementation • 14 Dec 2018 • Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong
Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines.