Here is a list of publications, listed in reverse order of publication. I also have a Google Scholar profile although the information there may not always be up-to-date.

• R.R. Curtin, M. Edel, R. Prabhu, S. Basak, Z. Lou, C. Sanderson. ``The ensmallen library for flexible numerical optimization''. Accepted to The Journal of Machine Learning Research (JMLR), 2021.
• M. Abo Khamis, R.R. Curtin, S. Im, B. Moseley, H. Ngo, K. Pruhs, A. Samadian. ``An Approximation Algorithm for the Matrix Tree Multiplication Problem''. Accepted to The 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021), 2021.
• C. Sanderson, R.R. Curtin. "An Adaptive Solver for Systems of Linear Equations". In The 14th International Conference on Signal Processing and Communication Systems (ICSPCS '20), pp. 1--6, 2020. [pdf]
• R.R. Curtin, M. Edel, R.G. Prabhu, S. Basak, Z. Lou, C. Sanderson. "Flexible numerical optimization with ensmallen". arXiv preprint arXiv:2003.04103, 2020. [pdf]
• M.A. Khamis, R.R. Curtin, B. Moseley, H.Q. Ngo, X. Nguyen, D. Olteanu, M. Schleich. "Functional Aggregate Queries with Additive Inequalities". ACM Transactions on Database Systems (TODS), 45.4, pp. 1--41, 2020.
• R.R. Curtin, B. Moseley, H.Q. Ngo, X.L. Nguyen, D. Olteanu, M. Schleich. "Rk-means: Fast Clustering for Relational Data". In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), p. 2742-2752, 2020. [pdf]
• A. Samadian, K. Pruhs, B. Moseley, S. Im, R.R. Curtin. "On Coresets for Regularized Loss Minimization". In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), p. 482-492, 2020.. [pdf]
• C. Sanderson, R.R. Curtin. "Practical Sparse Matrices in C++ with Hybrid Storage and Templated-Based Expression Optimisation". Mathematical and Computational Applications, vol. 24, no. 3, article 70, 2019. [pdf] [html] [bib] [code]
• M.A. Khamis, R.R. Curtin, B. Moseley, H.Q. Ngo, X.L. Nguyen, D. Olteanu, M. Schleich. "On functional aggregate queries with additive inequalities". Proceedings of The 2019 ACM SIGMOD/PODS International Conference on Management of Data, 2019. [pdf]
• R.R. Curtin, A.B. Gardner, S. Grzonkowski, A. Kleymenov, A. Mosquera. "Detecting DGA domains with recurrent neural networks and side information". The 14th International Conference on Availability, Reliability, and Security (ARES 2019), p. 1-10, 2019.
• S. Bhardwaj, R.R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson. "ensmallen: a flexible C++ library for efficient function optimization", Systems for ML Workshop at NeurIPS 2018, 2018. [pdf] [bib] [code]
• R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. "mlpack 3: a fast, flexible machine learning library", The Journal of Open Source Software, vol. 3, issue 26, pp. 726, 2018. [pdf] [bib] [code]
• C. Sanderson, R.R. Curtin. "A User-Friendly Hybrid Sparse Matrix Class in C++", Proceedings of The 2018 International Congress on Mathematical Software (ICMS 2018), p. 422-430, 2018. [pdf] [bib][code]
• R.R. Curtin, J. Echauz, A.B. Gardner. "Exploiting the structure of furthest neighbor search for fast approximate results", Information Systems, 2018. [pdf] [code]
• C. Sanderson, R.R. Curtin. "gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation", The Journal of Open Source Software, vol. 2, 2017. [code] [paper]
• C. Sanderson, R.R. Curtin. "An open source C++ implementation of multi-threaded Gaussian Mixture Models, k-means and expectation maximisation", Proceedings of the 11th International Conference on Signal Processing and Communication Systems (ICSPCS 2017), p. 1-8. [code]
• R.R. Curtin, M. Edel. "Designing and building the mlpack open-source machine learning library", submitted to The Fourth International Conference of PUST (ICOPUST '17). [pdf] [code]
• J. Echauz, A.B. Gardner, R.R. Curtin, N. Vasiloglou, G.J. Vachtsevanos. "PFsuper: simulation-based prognostics to monitor and predict sparse time series", in Annual Conference of the Prognostics and Health Management Society 2017 (PHM '17), 2017, p. 1--9. [pdf]
• R. Feinman, R.R. Curtin, S. Shintre, A.B. Gardner. "Detecting adversarial samples from artifacts", arXiv preprint arXiv:1703.00410, 2017. [pdf]
• R.R. Curtin. A dual-tree algorithm for fast k-means clustering with large k", In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM '17), p. 300-308, 2017. 2017. [pdf] [code]
• C. Sanderson, R.R. Curtin. "Armadillo: a template-based C++ library for linear algebra", Journal of Open Source Software, vol. 1:26, pp. 1--2, 2016. [pdf] [bib] [code]
• R.R. Curtin, A.B. Gardner. "Fast approximate furthest neighbors with data-dependent candidate selection", in Similarity Search and Applications 2016 (SISAP 2016), 2016, p. 221--235. [pdf] [bib]
• R.R. Curtin. "Improving dual-tree algorithms", Ph.D. thesis, Georgia Institute of Technology, 2015. [pdf] [bib]
• R.R. Curtin. "Faster dual-tree traversal for nearest neighbor search", in Similarity Search and Applications (SISAP 2015), 2015, p. 77-89. [pdf] [bib]
• R.R. Curtin. "Single-tree GMM training", technical report GT-CSE-2015-01, Georgia Institute of Technology, School of Computational Science and Engineering, 2015. [pdf] [bib]
• S. Agrawal, R.R. Curtin, S. Ghaisas, M.R. Gupta. "Collaborative filtering via matrix decomposition in mlpack", Workshop on Machine Learning Open Source Software 2015, 2015. [pdf] [bib] [code]
• R.R. Curtin, D. Lee, W.B. March, P. Ram. "Plug-and-play runtime analysis for dual-tree algorithms", in The Journal of Machine Learning Research, vol. 16, p. 3269-3297, 2015. [pdf] [bib]
• M. Edel, A. Soni, R.R. Curtin. "An automatic benchmarking system", in NIPS 2014 Workshop on Software Engineering for Machine Learning, 2014. [pdf] [code]
• R.R. Curtin, W. Daley, D.V. Anderson. "Classifying broiler chicken condition using audio data", in GlobalSIP 2014 Symposium on Signal Processing Applications Related to Animal Environments, 2014. [pdf] [bib]
• R.R. Curtin, P. Ram, "Dual-tree fast max-kernel search", Statistical Analysis and Data Mining, vol. 7, issue 4, p. 229-253, 2014. [bib] [pdf] [code]
• R.R. Curtin, W.B. March, P. Ram, D.V. Anderson, A.G. Gray, C.L. Isbell, Jr., "Tree-independent dual-tree algorithms", in The 30th International Conference on Machine Learning (ICML '13), Atlanta, Georgia, 2013. [bib] [pdf]
• R.R. Curtin, J.R. Cline, N.P. Slagle, W.B. March, P. Ram, N.A. Mehta, A.G. Gray, "mlpack: a scalable C++ machine learning library", The Journal of Machine Learning Research (JMLR), vol. 14, p. 801-805, 2013. [bib] [pdf] [code]
• R.R. Curtin, P. Ram, A.G. Gray, "Fast exact max-kernel search", in SIAM Data Mining 2013 (SDM '13), Austin, Texas, 2013. [bib] [pdf]
• R.R. Curtin, J.R. Cline, N.P. Slagle, M.L. Amidon, A.G. Gray, "mlpack: a scalable C++ machine learning library", in NIPS 2011 Workshop on Big Learning, Granada, Spain, 2011. [bib] [pdf] [code]
• R.R. Curtin, N. Vasiloglou, D.V. Anderson, "Learning distances to improve phoneme classification", in Proceedings of the 2011 IEEE International Workshop on Machine Learning in Signal Processing (MLSP 2011), Beijing, China, 2011. [bib] [pdf]