Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep variational information bottleneck. In Proceedings of the 5th International Conference on Learning Representations (ICLR), 2017.
Ferran Alet, Erica Weng, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. Neural relational inference with fast modular meta-learning. In Advances in Neural Information Processing Systems 32 (NeurIPS), 2019.
Theodore Wilbur Anderson. An Introduction to Multivariate Statistical Analysis, volume 2. Wiley New York, 1958.
James Biagioni and Jakob Eriksson. Inferring road maps from global positioning system traces: Survey and comparative evaluation. Transportation research record, 2291(1):61-71, 2012.
Guillem Brasó and Laura Leal-Taixé. Learning a neural solver for multiple object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6247-6257, 2020.
Thalia E Chan, Michael PH Stumpf, and Ann C Babtie. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Systems, 5(3):251-267, 2017.
Chao Chen, Karl Petty, Alexander Skabardonis, Pravin Varaiya, and Zhanfeng Jia. Freeway performance measurement system: mining loop detector data. Transportation Research Record, 1748 (1):96-102, 2001.
Siyuan Chen, Jiahai Wang, and Guoqing Li. Neural relational inference with efficient message passing mechanisms. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), pp. 7055-7063, 2021.
Tianqi Chen and Carlos Guestrin. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 785-794. ACM, 2016.
P ERDdS and A R&wi. On random graphs i. Publ. math. debrecen, 6(290-297):18, 1959.
Jeremiah J Faith, Boris Hayete, Joshua T Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J Collins, and Timothy S Gardner. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 5(1):e8, 2007.
Seungwoong Ha and Hawoong Jeong. Unraveling hidden interactions in complex systems with deep learning. Scientific Reports, 11(1):1-13, 2021.
Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona, and Jean-Philippe Vert. TIGRESS: trustful inference of gene regulation using stability selection. BMC Systems Biology, 6(1):1-17, 2012.
Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan, Brooke E. Husic, and Vijay S. Pande. Variational encoding of complex dynamics. Physical Review E, 97(6):062412, 2018.
W. Huber, V. J. Carey, R. Gentleman, S. Anders, M. Carlson, B. S. Carvalho, H. C. Bravo, S. Davis, L. Gatto, T. Girke, R. Gottardo, F. Hahne, K. D. Hansen, R. A. Irizarry, M. Lawrence, M. I. Love, J. MacDonald, V. Obenchain, A. K. Ole's, H. Pag'es, A. Reyes, P. Shannon, G. K. Smyth, D. Tenenbaum, L. Waldron, and M. Morgan. Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods, 12(2):115-121, 2015.
Vân Anh Huynh-Thu and Pierre Geurts. dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific Reports, 8(1):3384, 2018.
Seongho Kim. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Communications for Statistical Applications and Methods, 22(6):665, 2015.
Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel. Neural relational inference for interacting systems. In Proceedings of the 35th International Conference on Machine Learning (ICML), pp. 2688-2697. PMLR, 2018.
Jarosław Kwapień and Stanisław Drozdz. Physical approach to complex systems. Physics Reports, 515(3):115-226, 2012.
Jiachen Li, Hengbo Ma, Zhihao Zhang, Jinning Li, and Masayoshi Tomizuka. Spatio-temporal graph dual-attention network for multi-agent prediction and tracking. IEEE Transactions on Intelligent Transportation Systems, 23(8):10556-10569, 2022.
Kuan Liu, Haiyuan Liu, Dongyan Sun, and Lei Zhang. Network inference from gene expression data with distance correlation and network topology centrality. Algorithms, 14(2), 2021.
Sindy Löwe, David Madras, Richard Z. Shilling, and Max Welling. Amortized causal discovery: Learning to infer causal graphs from time-series data. In Proceedings of the 1st Conference on Causal Learning and Reasoning (CLeaR), pp. 509-525. PMLR, 2022.
Baoshan Ma, Mingkun Fang, and Xiangtian Jiao. Inference of gene regulatory networks based on nonlinear ordinary differential equations. Bioinformatics, 36(19):4885-4893, 2020.
Adam A Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7:1-15, 2006.
Markus Maucher, Barbara Kracher, Michael Kühl, and Hans A. Kestler. Inferring Boolean network structure via correlation. Bioinformatics, 27(11):1529-1536, 04 2011.
Patrick E Meyer, Frederic Lafitte, and Gianluca Bontempi. minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC bioinformatics, 9:1-10, 2008.
Tristan Millington and Mahesan Niranjan. Quantifying influence in financial markets via partial correlation network inference. In Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 306-311. IEEE CS, 2019.
Frank Noé and Feliks Nuske. A variational approach to modeling slow processes in stochastic dynamical systems. Multiscale Modeling & Simulation, 11(2):635-655, 2013.
Nan Papili Gao, SM Minhaz Ud-Dean, Olivier Gandrillon, and Rudiyanto Gunawan. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics, 34(2):258-266, 2018.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
Aditya Pratapa, Amogh P. Jalihal, Jeffrey N. Law, Aditya Bharadwaj, and T.M. Murali. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods, 17(2):147-154, 2020.
Xiaojie Qiu, Arman Rahimzamani, Li Wang, Bingcheng Ren, Qi Mao, Timothy Durham, José L McFaline-Figueroa, Lauren Saunders, Cole Trapnell, and Sreeram Kannan. Inferring causal gene regulatory networks from coupled single-cell expression dynamics using Scribe. Cell Systems, 10 (3):265-274, 2020.
Arman Rahimzamani and Sreeram Kannan. Network inference using directed information: The deterministic limit. In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 156-163. IEEE, 2016.
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. A comparison of single-cell trajectory inference methods. Nature Biotechnology, 37(5):547-554, 2019.
Marco Sarich, Frank Noé, and Christof Schütte. On the approximation quality of markov state models. Multiscale Modeling & Simulation, 8(4):1154-1177, 2010.
David J. Sheskin. Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and hall/CRC, 2003.
Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017.
Stephen M Smith, Karla L Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F Beckmann, Thomas E Nichols, Joseph D Ramsey, and Mark W Woolrich. Network modelling methods for FMRI. Neuroimage, 54(2):875-891, 2011.
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 914-921, 2020.
Alicia T Specht and Jun Li. Leap: constructing gene co-expression networks for single-cell rnasequencing data using pseudotime ordering. Bioinformatics, 33(5):764-766, 2017.
Attila Szabo and Neil S Ostlund. Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Courier Corporation, 2012.
Bishenghui Tao, Hong-Ning Dai, Jiajing Wu, Ivan Wang-Hei Ho, Zibin Zheng, and Chak Fong Cheang. Complex network analysis of the bitcoin transaction network. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3):1009-1013, 2021.
Naftali Tishby and Noga Zaslavsky. Deep learning and the information bottleneck principle. In Proceedings of 2015 IEEE Information Theory Workshop (ITW), pp. 1-5. IEEE, 2015.
Naftali Tishby, F.C. Pereira, and W. Biale. The information bottleneck method. In Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 368-377. IEEE, 1999.
Masashi Tsubaki, Kentaro Tomii, and Jun Sese. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 35(2):309-318, 2019.
S. Varrette, H. Cartiaux, S. Peter, E. Kieffer, T. Valette, and A. Olloh. Management of an Academic HPC & Research Computing Facility: The ULHPC Experience 2.0. In Proceedings of the 2022 6th High Performance Computing and Cluster Technologies Conference. (HPCCT 2022), Fuzhou, China, July 2022. Association for Computing Machinery (ACM). ISBN 978-1-4503-9664-6.
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261-272, 2020.
Aoran Wang and Jun Pang. Iterative structural inference of directed graphs. In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.
Aoran Wang and Jun Pang. Active learning based structural inference. In Proceedings of the 40th International Conference on Machine Learning (ICML), pp. 36224-36245. PMLR, 2023.
Aoran Wang, Tsz Pan Tong, and Jun Pang. Effective and efficient structural inference with reservoir computing. In Proceedings of the 40th International Conference on Machine Learning (ICML), pp. 36391-36410. PMLR, 2023a.
Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Xiaojiang Peng, Yuxuan Liang, and Yang Wang. The snowflake hypothesis: Training deep GNN with one node one receptive field. arXiv preprint arXiv:2308.10051, 2023b.
Charles Ernest Weatherburn. A First Course Mathematical Statistics, volume 158. Cambridge University Press Archive, 1949.
Ezra Webb, Ben Day, Helena Andres-Terre, and Pietro Lió. Factorised neural relational inference for multi-interaction systems. arXiv preprints arXiv:1905.08721, 2019.
Joe Whittaker. Graphical Models in Applied Multivariate Statistics. Wiley, 1990.
Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. In Proceedings of the European conference on computer vision (ECCV), pp. 670-685, 2018.
Yiming Zuo, Guoqiang Yu, Mahlet G. Tadesse, and Habtom W. Ressom. Biological network inference using low order partial correlation. Methods, 69(3):266-273, 2014.
