1 code implementation • 25 Feb 2024 • Jiahe Lin, Huitian Lei, George Michailidis
Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, and the focus in extant literature has been on a single dynamical system.
no code implementations • 8 Nov 2022 • Parvin Nazari, Ahmad Mousavi, Davoud Ataee Tarzanagh, George Michailidis
A key feature of the proposed algorithm is to estimate the hyper-gradient of the penalty function via decentralized computation of matrix-vector products and few vector communications, which is then integrated within an alternating algorithm to obtain finite-time convergence analysis under different convexity assumptions.
no code implementations • 26 Jun 2022 • Kailash Budhathoki, George Michailidis, Dominik Janzing
Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output).
no code implementations • 9 Feb 2022 • Michael Weylandt, George Michailidis
Remarkably, we show that SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with error proportional to the square root of the number of vertices in the network and not the number of edges as might be expected.
no code implementations • 21 Dec 2021 • Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Linear time-invariant systems are very popular models in system theory and applications.
no code implementations • 19 Jul 2021 • Abhishek Kaul, George Michailidis
Component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions for any finite subset of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates.
no code implementations • 10 Jun 2021 • Babak Barazandeh, Tianjian Huang, George Michailidis
Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs).
no code implementations • 26 Apr 2021 • Babak Barazandeh, Davoud Ataee Tarzanagh, George Michailidis
Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks.
no code implementations • 6 Apr 2021 • Michael Weylandt, George Michailidis, T. Mitchell Roddenberry
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains.
no code implementations • 8 Dec 2020 • Michael Weylandt, George Michailidis
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals.
no code implementations • 3 Dec 2020 • Rohit K. Patra, Moulinath Banerjee, George Michailidis
In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target.
Disaster Response Methodology
no code implementations • 19 May 2020 • Parvin Nazari, Davoud Ataee Tarzanagh, George Michailidis
In this paper, we design and analyze a new family of adaptive subgradient methods for solving an important class of weakly convex (possibly nonsmooth) stochastic optimization problems.
no code implementations • 19 May 2020 • Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis
We develop an estimator for the change point parameter for a dynamically evolving graphical model, and also obtain its asymptotic distribution under high dimensional scaling.
no code implementations • 16 Mar 2020 • Hossein Keshavarz, George Michailidis
The problem of identifying change points in high-dimensional Gaussian graphical models (GGMs) in an online fashion is of interest, due to new applications in biology, economics and social sciences.
1 code implementation • 9 Dec 2019 • Jiahe Lin, George Michailidis
A factor-augmented vector autoregressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations amongst a set of observed variables $X$ and latent factors $F$, and a calibration equation that relates another set of observed variables $Y$ with $F$ and $X$.
Methodology Econometrics
1 code implementation • 24 Nov 2019 • Davoud Ataee Tarzanagh, George Michailidis
We introduce a general tensor model suitable for data analytic tasks for {\em heterogeneous} datasets, wherein there are joint low-rank structures within groups of observations, but also discriminative structures across different groups.
1 code implementation • 17 Jun 2019 • Clint P. George, Wei Xia, George Michailidis
The usability study on some real-world corpora illustrates the superiority of cLDA to explore the underlying topics automatically but also model their connections and variations across multiple collections.
no code implementations • 17 May 2019 • Davoud Ataee Tarzanagh, Mohamad Kazem Shirani Faradonbeh, George Michailidis
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications.
no code implementations • 16 May 2019 • Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
We provide numerical analyses for the performance of two methods: stochastic feedback, and stochastic parameter.
no code implementations • 14 Mar 2019 • Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
In decision making problems for continuous state and action spaces, linear dynamical models are widely employed.
1 code implementation • ICLR 2019 • Parvin Nazari, Davoud Ataee Tarzanagh, George Michailidis
Adaptive gradient-based optimization methods such as \textsc{Adagrad}, \textsc{Rmsprop}, and \textsc{Adam} are widely used in solving large-scale machine learning problems including deep learning.
no code implementations • 7 Dec 2018 • Monika Bhattacharjee, Moulinath Banerjee, George Michailidis
Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters.
no code implementations • 10 Nov 2018 • Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
This paper studies adaptive algorithms for simultaneous regulation (i. e., control) and estimation (i. e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems.
no code implementations • 22 Jul 2018 • Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
There are only a few existing non-asymptotic results and a full treatment of the problem is not currently available.
no code implementations • 20 Jun 2018 • Hossein Keshavarz, George Michailidis, Yves Atchade
High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences.
1 code implementation • 9 Mar 2018 • Subhabrata Majumdar, George Michailidis
Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer.
no code implementations • 20 Nov 2017 • Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control.
no code implementations • 2 Dec 2013 • Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis, George Michailidis
The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data.
no code implementations • 24 Jun 2013 • Michael Kallitsis, Stilian Stoev, George Michailidis
The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size.
no code implementations • 30 May 2013 • Shawn Mankad, George Michailidis
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction.
no code implementations • NeurIPS 2010 • Ali Shojaie, George Michailidis
Network models are widely used to capture interactions among component of complex systems, such as social and biological.