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2021 Conference Publication Shadow Manifold Hamiltonian Monte Carlovan der Heide, Chris, Hodgkinson, Liam, Roosta, Fred and Kroese, Dirk (2021). Shadow Manifold Hamiltonian Monte Carlo. International Conference on Artificial Intelligence and Statistics, Online, 27-30- July 2021. Tempe, AZ United States: ML Research Press. |
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2021 Journal Article Evolution and application of digital technologies to predict crop type and crop phenology in agriculturePotgieter, A. B., Zhao, Yan, Zarco-Tejada, Pablo J, Chenu, Karine, Zhang, Yifan, Porker, Kenton, Biddulph, Ben, Dang, Yash P., Neale, Tim, Roosta, Fred and Chapman, Scott (2021). Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. In Silico Plants, 3 (1) diab017, 1-23. doi: 10.1093/insilicoplants/diab017 |
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2021 Journal Article Inexact nonconvex Newton-type methodsYao, Zhewei, Xu, Peng, Roosta, Fred and Mahoney, Michael W. (2021). Inexact nonconvex Newton-type methods. INFORMS Journal on Optimization, 3 (2), 154-182. doi: 10.1287/ijoo.2019.0043 |
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2021 Journal Article Convergence of Newton-mr under inexact hessian informationLiu, Yang and Roosta, Fred (2021). Convergence of Newton-mr under inexact hessian information. SIAM Journal on Optimization, 31 (1), 59-90. doi: 10.1137/19M1302211 |
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2021 Conference Publication Avoiding kernel fixed points: Computing with ELU and GELU infinite networksTsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: Computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Online, 2 - 9 February 2021. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v35i11.17197 |
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2021 Conference Publication Avoiding kernel fixed points: computing with ELU and GELU infinite networksTsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, 2-9 February 2021. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. |
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2021 Journal Article Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddingsLevin, Keith D., Roosta, Fred, Tang, Minh, Mahoney, Michael W. and Priebe, Carey E. (2021). Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. Journal of Machine Learning Research, 22 194, 1-59. |
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2021 Conference Publication Stochastic continuous normalizing flows: training SDEs as ODEsHodgkinson, Liam, van der Heide, Chris, Roosta, Fred and Mahoney, Michael W. (2021). Stochastic continuous normalizing flows: training SDEs as ODEs. Conference on Uncertainty in Artificial Intelligence, Online, 27-29 July 2021. San Diego, CA, United States: Association For Uncertainty in Artificial Intelligence (AUAI). |
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2021 Conference Publication Non-PSD matrix sketching with applications to regression and optimizationFeng, Zhili, Roosta, Fred and Woodruff, David P. (2021). Non-PSD matrix sketching with applications to regression and optimization. Conference on Uncertainty in Artificial Intelligence, Online, 27-29 July 2021. San Diego, CA United States: Association For Uncertainty in Artificial Intelligence (AUAI). |
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2020 Journal Article Newton-type methods for non-convex optimization under inexact Hessian informationXu, Peng, Roosta, Fred and Mahoney, Michael W. (2020). Newton-type methods for non-convex optimization under inexact Hessian information. Mathematical Programming, 184 (1-2), 35-70. doi: 10.1007/s10107-019-01405-z |
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2020 Conference Publication Newton-admm: a distributed GPU-accelerated optimizer for multiclass classification problemsFang, Chih-Hao, Kylasa, Sudhir B., Roosta, Fred, Mahoney, Michael W. and Grama, Ananth (2020). Newton-admm: a distributed GPU-accelerated optimizer for multiclass classification problems. International Conference on High Performance Computing, Networking, Storage and Analysis (SC), Atlanta, GA, United States, 9-19 November 2020. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/SC41405.2020.00061 |
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2020 Conference Publication DINO: Distributed Newton-type optimization methodCrane, Rixon and Roosta, Fred (2020). DINO: Distributed Newton-type optimization method. International Conference on Machine Learning, Online, 12-18 July 2020. San Diego, CA United States: International Conference on Machine Learning. |
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2020 Conference Publication DINO: Distributed Newton-type optimization methodCrane, Rixon and Roosta, Fred (2020). DINO: Distributed Newton-type optimization method. 37th International Conference on Machine Learning, ICML 2020, Online, 12-18 July 2020. International Machine Learning Society. |
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2020 Book Chapter Parallel optimization techniques for machine learningKylasa, Sudhir, Fang, Chih-Hao, Roosta, Fred and Grama, Ananth (2020). Parallel optimization techniques for machine learning. Parallel algorithms in computational science and engineering. (pp. 381-417) edited by Ananth Grama and Ahmed H. Sameh. Cham, Switzerland: Birkhauser. doi: 10.1007/978-3-030-43736-7_13 |
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2020 Conference Publication Second-order optimization for non-convex machine learning: an empirical studyXu, Peng, Roosta, Fred and Mahoney, Michael W. (2020). Second-order optimization for non-convex machine learning: an empirical study. SIAM International Conference on Data Mining, Cincinnati, OH, United States, 7-9 May 2020. Philadelphia, PA, United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611976236.23 |
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2019 Conference Publication GPU accelerated sub-sampled Newton's method for convex classification problemsKylasa, Sudhir, Roosta, Fred (Farbod), Mahoney, Michael W. and Grama, Ananth (2019). GPU accelerated sub-sampled Newton's method for convex classification problems. SIAM International Conference on Data Mining, Calgary, Canada, 2-4 May 2019. Philadelphia, PA, United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611975673.79 |
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2019 Book Chapter Optimization methods for inverse problemsYe, Nan, Roosta-Khorasani, Farbod and Cui, Tiangang (2019). Optimization methods for inverse problems. 2017 MATRIX annals. (pp. 121-140) edited by David R. Wood, Jan de Gier, Cheryl E. Praeger and Terence Tao. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04161-8_9 |
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2019 Conference Publication DINGO: Distributed Newton-type method for gradient-norm optimizationCrane, Rixon and Roosta, Fred (2019). DINGO: Distributed Newton-type method for gradient-norm optimization. Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 8-14 December 2019. Maryland Heights, MO United States: Morgan Kaufmann Publishers. |
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2019 Conference Publication Exchangeability and kernel invariance in trained MLPsTsuchida, Russell, Roosta, Fred and Gallagher, Marcus (2019). Exchangeability and kernel invariance in trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, Macao, China, 10-16 August 2019. Marina del Rey, CA USA: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2019/498 |
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2018 Journal Article Sub-sampled Newton methodsRoosta-Khorasani, Farbod and Mahoney, Michael W. (2018). Sub-sampled Newton methods. Mathematical Programming, 174 (1-2), 293-326. doi: 10.1007/s10107-018-1346-5 |
