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2024 Conference Publication Manifold integrated gradients: Riemannian geometry for feature attributionZaher, Eslam, Trzaskowski, Maciej, Nguyen, Quan and Roosta, Fred (2024). Manifold integrated gradients: Riemannian geometry for feature attribution. International Conference on Machine Learning, Vienna, Austria, 21-27 July 2024. Proceedings of Machine Learning Research. |
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2024 Conference Publication Inexact Newton-type methods for optimisation with nonnegativity constraintsSmee, Oscar and Roosta, Fred (2024). Inexact Newton-type methods for optimisation with nonnegativity constraints. International Conference on Machine Learning, Vienna, Austria, 21-27 July 2024. Proceedings of Machine Learning Research. |
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2023 Conference Publication Monotonicity and double descent in uncertainty estimation with gaussian processesHodgkinson, Liam, Van Der Heide, Chris, Roosta, Fred and Mahoney, Michael W. (2023). Monotonicity and double descent in uncertainty estimation with gaussian processes. International Conference on Machine Learning, Honolulu, HI United States, 23 - 29 July 2023. San Diego, CA United States: International Conference on Machine Learning. |
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2022 Conference Publication Crop type prediction utilising a long short-term memory with a self-attention for winter crops in AustraliaNguyen, Dung, Zhao, Yan, Zhang, Yifan, Huynh, Anh Ngoc-Lan, Roosta, Fred, Hammer, Graeme, Chapman, Scott and Potgieter, Andries (2022). Crop type prediction utilising a long short-term memory with a self-attention for winter crops in Australia. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17-22 July 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IGARSS46834.2022.9883737 |
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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 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. |
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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 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 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 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, Virtual, 12-18 July 2020. San Diego, CA, United States: International Conference on Machine Learning. |
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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 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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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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2018 Conference Publication FLAG n’ FLARE: fast linearly-coupled adaptive gradient methodsCheng, Xiang, Roosta-Khorasani, Farbod, Palombo, Stefan, Bartlett, Peter L. and Mahoney, Michael W. (2018). FLAG n’ FLARE: fast linearly-coupled adaptive gradient methods. Twenty-First International Conference on Artificial Intelligence and Statistics, Lanzarote, Canary Islands, 9-11 April 2018. Cambridge, MA, United States: M I T Press. |
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2018 Conference Publication Invariance of weight distributions in rectified MLPsTsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press. |
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2018 Conference Publication GIANT: Globally improved approximate Newton method for distributed optimizationWang, Shusen, Roosta-Khorasani, Farbod, Xu, Peng and Mahoney, Michael W. (2018). GIANT: Globally improved approximate Newton method for distributed optimization. 32nd Conference on Neural Information Processing Systems, NeurIPS 2018, Montreal, QC, Canada, 2 - 8 December, 2018. Maryland Heights, MO, United States: Neural information processing systems foundation. |
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2018 Conference Publication Out-of-sample extension of graph adjacency spectral embeddingLevin, Keith, Roosta-Khorasani, Farbod, Mahoney, Michael W. and Priebe, Carey E. (2018). Out-of-sample extension of graph adjacency spectral embedding. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press. |
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2017 Conference Publication The Union of Intersections (UoI) method for interpretable data driven discovery and predictionBouchard, Kristofer E, Bujan, Alejandro F, Roosta-Khorasani, Farbod, Prabhat, Snijders, Jian-Hua Mao, Chang, Edward F, Mahoney, Michael W and Bhattacharyya, Sharmodeep (2017). The Union of Intersections (UoI) method for interpretable data driven discovery and prediction. 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA United States, 4-9 December 2017. Maryland Heights, MO, United States: Morgan Kaufmann Publishers. |
