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Publications 2024 P. Rumiantsev, M. Coates, Graph Knowledge Distillation to Mixture of Experts Trans. Mach. Learn. Res. (TMLR), 2024. [code ] L. Ma, S. Pal, Y. Zhang, J. Zhou, Y. Zhang, M. Coates, CKGConv: General Graph Convolution with Continuous Kernels. Proc. Int. Conf. Mach. Learn. (ICML), 2024. [code ] M. Zeng, F. Regol , M. Coates, Interacting Diffusion Processes for Event Sequence Forecasting. Proc. Int. Conf. Mach. Learn. (ICML), 2024. F. Regol, J. Chataoui , M. Coates, Jointly-Learned Exit and Inference for a Dynamic Neural Network Proc. Int. Conf. Learn. Repr. (ICLR), 2024. [code ] Y. Zhang, L. Ma , S. Pal, Y. Zhang, M. Coates, Multi-resolution Time-Series Transformer for Long-term Forecasting. Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2024. [code ] F. Regol, M. Coates, Categorical Generative Model Evaluation via Synthetic Distribution Coarsening Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2024. 2023 K. Zahirnia, Y. Hu, M. Coates and O. Schulte, Neural Graph Generation from Graph Statistics. Adv. Neural Inf. Process. Syst. (NeurIPS), 2023. M. Ozmen, J. Cotnareanu, M. Coates, Substituting Data Annotation with Balanced Neighbourhoods and Collective Loss in Multi-label Text Classification. Proc. Conf. Lifelong Learn. Agents (CoLLAs), 2023. S. Pal, A. Valkanas and M. Coates, Population Monte Carlo with Normalizing Flow. Signal Processing Letters , 2023. [code ] M. Alomrani, M. Biparva, Y. Zhang and M. Coates, DyG2Vec: Representation Learning for Dynamic Graphs with Self-supervision. Trans. Mach. Learn. Res. (TMLR), 2023. B. Oreshkin, A. Valkanas, F. Harvey, L-S. Ménard, F. Bocquelet, and M. Coates, Motion In-Betweening via Deep Δ-Interpolator. IEEE Trans. Visualization and Computer Graphics , 2023. [code ] Ma L, Lin C, Lim D, Romero-Soriano A, Dokania PK, Coates M, Torr P, Lim SN. Graph Inductive Biases in Transformers without Message Passing. Proc. Int. Conf. Mach. Learn. (ICML), 2023. [code ] C. Chen, Y. Zhang, X. Liu, M. Coates, Bidirectional Learning for Offline Model-based Biological Sequence Design. Proc. Int. Conf. Mach. Learn. (ICML), 2023. M. Mehrabi, W. Masoudimansour, Y. Zhang, J. Chuai, Z. Chen, M. Coates, J. Hao, Y. Geng, Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network. Proc. AAAI Int. Conf. Artif. Intell. (AAAI), 2023. Regol F, Coates M. Diffusing gaussian mixtures for generating categorical data. Proc. AAAI Conf. on Artif. Intell. (AAAI)., 2023. F. Regol, A. Kroon, and M. Coates, Evaluation of categorical generative models – bridging the gap between real and synthetic data. Proc. Int. Conf. Acoustics, Speech, Signal Proc. (ICASSP), 2023. P Rumiantsev, M Coates. Performing Neural Architecture Search Without Gradients. Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , 2023 [code ] A. Ghose, Y. Zhang, J. Hao, M. Coates, Spectral Augmentations for Graph Contrastive Learning. Proc. Int. Conf. Artif. Intell. Statistics (AISTATS), 2023. Y. Wang, Y. Zhang, A. Valkanas, R. Tang, C. Ma, J. Hao, M. Coates, Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems. Proc. AAAI Int. Conf. Artif. Intell. , 2023. H. Wu, Y. Zhang, C. Ma, W. Guo, R. Tang, X. Liu, M. Coates, Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation. Proc. Int. Conf. Data Engineering (ICDE), 2023. 2022 H. Wu, C. Ma, Y. Zhang, X. Liu, and M. Coates, Adapting triplet importance of implicit feedback for personalized recommendation. Proc. ACM Int. Conf. Inf. Knowl. Manag. (CIKM), 2022. C. Chen, Y. Zhang, J. Fu, X. Liu, and M. Coates, Bidirectional learning for offline infinite-width model-based optimization. Adv. Neural Inf. Process. Syst. (NeruIPS), 2022. Chamroukhi F, Brivet S, Savadjiev P, Coates M, Forghani R. DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation. Diagnostics . 2022 Dec;12(12):3072. [pdf ] Oreshkin BN, Valkanas A, Harvey FG, Ménard LS, Bocquelet F, Coates MJ. Motion Inbetweening via Deep Δ \Delta Δ arXiv preprint arXiv:2201.06701. 2022 Jan 18. [pdf ] Brivet S, Chamroukhi F, Coates M, Forghani R, Savadjiev P. Spectral image clustering on dual-energy CT scans using functional regression mixtures. arXiv preprint arXiv:2201.13398. 2022 Jan 31. [pdf ] Regol F, Pal S, Sun J, Zhang Y, Geng Y, Coates M. Node copying: A random graph model for effective graph sampling. Signal Process. , 2022 Mar 1;192:108335. [pdf ] Ozmen M, Zhang H, Wang P, Coates M. Multi-relation message passing for multi-label text classification. Proc. IEEE Int. Conf. Acoust. Speech Signal Process.(ICASSP) , 2022. [pdf ] Pal S, Valkanas A, Regol F, Coates M. Bag graph: Multiple instance learning using bayesian graph neural networks. Proc. AAAI Conf. Artif. Intell. , 2022. [pdf ][code ] Zhang Y, Regol F, Valkanas A, Coates M. Contrastive Learning for Time Series on Dynamic Graphs. Proc. Euro. Signal Process. Conf.(EUSIPCO) , 2022. IEEE. [pdf ] Regol F, Kroon A, Coates M. Evaluation of Categorical Generative Models--Bridging the Gap Between Real and Synthetic Data. arXiv preprint arXiv:2210.16405. , 2022 Oct 28. [pdf ] 2021 Oreshkin BN, Amini A, Coyle L, Coates M. FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting. Proc. AAAI Conf. Artif. Intell. , 2021. [pdf ] Ma C, Ma L, Zhang Y, Wu H, Liu X, Coates M. Knowledge-enhanced top-k recommendation in poincaré ball. Proc. AAAI Conf. Artif. Intell. , 2021. [pdf ] Pal S, Ma L, Zhang Y, Coates M. RNN with particle flow for probabilistic spatio-temporal forecasting. Proc. Int. Conf. Mach. Learn. , 2021. [pdf ] [code ] Zhang Y, Regol F, Pal S, Khan S, Ma L, Coates M. Detection and defense of topological adversarial attacks on graphs. Proc. Int. Conf. Artif. Intell. Stat. , 2021. [pdf ] Zhang Y, Wu H, Coates M. On the design of channel coding autoencoders with arbitrary rates for ISI channels. IEEE Commun. Lett. , 2021, 11(2). [pdf ] 2020 Regol F, Pal S, Zhang Y, Coates M. Active learning on attributed graphs via graph cognizant logistic regression and preemptive query generation. Proc. Int. Conf. Mach. Learn. , 2020. [pdf ] Kranold L, Ozmen M, Coates M, Popović M. Microwave Radar for Breast Health Monitoring: System Performance Protocol. Proc. IEEE MTT-S Int. Microw. Biomed. Conf. (IMBioC) , 2020. IEEE. [pdf ] Teimury F, Roy B, Casallas JS, MacDonald D, Coates M. GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping. arXiv preprint arXiv:2011.13748. , 2020 Nov 27. [pdf ] Mazza-Anthony C, Mazoure B, Coates M. Learning Gaussian Graphical Models With Ordered Weighted ℓ 1 \ell _1 ℓ 1 pdf ] Ma C, Ma L, Zhang Y, Tang R, Liu X, Coates M. Probabilistic metric learning with adaptive margin for top-k recommendation. Proc. ACM SIGKDD Int. Conf. Knowl Discov. Data Min. , 2020. [pdf ] Wu H, Zhang Y, Zhao X, Zhu N, Coates M. End-to-end Physical Layer Communication using Bi-directional GRUs for ISI Channels. IEEE Globecom Workshops (GC Wkshps) , 2020. IEEE. [pdf ] Valkanas A, Regol F, Coates M. Learning from networks of distributions. Asilomar Conf. Signals , Syst. Computers, 2020. IEEE. [pdf ] Pal S, Malekmohammadi S, Regol F, Zhang Y, Xu Y, Coates M. Non-parametric graph learning for Bayesian graph neural networks. Proc. Conf. Uncertainty Artif. Intell. , 2020. [pdf ] Kranold L, Taherzadeh M, Nabki F, Coates M, Popović M. Microwave breast screening prototype: System miniaturization with IC pulse radio. IEEE J. Electromagn. RF Microw. Med. Biol. , 2020. [pdf ] Wang Y, Yanushkevich S, Hou M, Plataniotis K, Coates M, Gavrilova M, Hu Y, Karray F, Leung H, Mohammadi A, Kwong S. A tripartite theory of trustworthiness for autonomous systems. IEEE Int. Conf. Syst. , Man, Cybern. (SMC), 2020. [pdf ] Ma C, Ma L, Zhang Y, Sun J, Liu X, Coates M. Memory augmented graph neural networks for sequential recommendation. Proc. AAAI Conf. Artif. Intell. , 2020. [pdf ] 2019 Teimury, F., S. Pal, A. Amini, and M. J. Coates, Estimation of time-series on graphs using Bayesian graph convolutional neural networks. Proc. SPIE Wavelets and Sparsity, 2019. [pdf ] Regol, F., S. Pal, and M. J. Coates, Node Copying for Protection Against Graph Neural Network Topology Attacks. Proc. IEEE Comput. Adv. in Multi-Sensor Adaptive Process. , 2019. [pdf ] Pal, S., and M. J. Coates, Particle Flow Particle Filter using Gromov’s method. Proc. IEEE Comput. Adv. in Multi-Sensor Adaptive Process. , 2019. [pdf ] Yu, J. Y., M. J. Coates, and M. G. Rabbat, Graph-Based Compression for Particle Filters IEEE Trans. Signal Inf. Process. Netw. , vol. 5, issue 3, 09/2019. [pdf ] Pal, S., F. Regol, and M. J. Coates, Bayesian graph convolutional neural networks using node copying Proc. Int. Conf. Mach. Learn., Learn. Reason. Graph-Structured Representations Workshop , 2019. [pdf ] Pal, S., F. Regol, and M. J. Coates, Bayesian graph convolutional neural networks using non-parametric graph learning Proc. Int. Conf. Learn. Rpresentations, Representation Learn. Graph Manifold Workshop , 2019. [pdf ] Li, Y., S. Pal, and M. J. Coates, Invertible particle-flow-based sequential MCMC with extension to Gaussian mixture noise models IEEE Trans. Signal Processing , vol. 67, issue 9, pp. 2499-2512, 05/2019. [pdf ] Pal, S., and M. J. Coates, Scalable MCMC in degree corrected stochastic block model Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP) ,2019. [pdf ] Zhang, Y., S. Pal, M. J. Coates, and D. Üstebay, Bayesian graph convolutional neural networks for semi-supervised classification Proc. AAAI Int. Conf. Artif. Intell., 2019. [pdf ] Assran, M., J. Romoff, N. Ballas, J. Pineau, and M. Rabbat, Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning Adv. Neural Inf. Process. Syst. , 2019. [pdf ] Valenchon, J., and M. J. Coates, Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP) , 2019. [pdf ] Assran, M., N. Loizou, N. Ballas, and M. G. Rabbat, Stochastic gradient push for distributed deep learning Proc. Int. Conf. Mach. Learn. , 2019. [pdf ]