Michael R. Metel

Research Interests: stochastic optimization, machine learning

Preprints:

1. Michael R. Metel, Variants of SGD for Lipschitz Continuous Loss Functions in Low-Precision Environments, https://arxiv.org/abs/2211.04655, 2022.

Publications: 

1. Michael R. Metel, Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint, Journal of Machine Learning Research, 24(103):1-44, 2023.

2. Michael R. Metel and Akiko Takeda, Perturbed Iterate SGD for Lipschitz Continuous Loss Functions, Journal of Optimization Theory and Applications, 195(2):504-547, 2022.

3. Michael R. Metel and Akiko Takeda, Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization, Journal of Machine Learning Research, 22(115):1-36, 2021.

4. Michael R. Metel and Akiko Takeda, Primal-dual subgradient method for constrained convex optimization problems, Optimization Letters, 15(4): 1491-1504, 2021.

5. Antoine Deza, Kai Huang, and Michael R. Metel, Charging station optimization for balanced electric car sharing, Discrete Applied Mathematics, 308: 187-197, 2022.

6. Michael R. Metel and Akiko Takeda, Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization, 36th International Conference on Machine Learning, 4537-4545, 2019.

7. Michael R. Metel, Kelly betting on horse races with uncertainty in probability estimates, Decision Analysis, 15(1): 47-52, 2018. 

8. Antoine Deza, Kai Huang, and Michael R. Metel, Managing losses in exotic horse race wagering, Journal of the Operational Research Society, 69(3): 319-325, 2018. 

9. Michael R. Metel, Traian A. Pirvu, and Julian Wong, Risk Management under Omega Measure, Risks, 5(2): 27, 2017. 

10. Antoine Deza, Kai Huang, and Michael R. Metel, Chance constrained optimization for targeted Internet advertising, Omega: The International Journal of Management Science, 53: 90-96, 2015.

Previous Experience:

Postdoctoral Researcher, RIKEN Center for Advanced Intelligence Project, Continuous Optimization Team, Tokyo, Japan, 2018-2021

Postdoctoral Fellow, Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay, France, 2016-2017.

PhD in Computational Science and Engineering, McMaster University, 2016.

Talks:

1. Workshop on Recent Advances in Optimization, Fields Institute, December 2022.

2. Workshop on Next Generation Optimization Techniques for the Social Implementation of Machine Learning Systems, Tokyo Institute of Technology, August 2019.

3. 6th International Conference on Continuous Optimization, Berlin, Germany, August 2019.

4. 2nd Conference on Discrete Optimization and Machine Learning, RIKEN AIP, July 2019.

5. 36th International Conference on Machine Learning, Long Beach, USA, June 2019.

6. PolyU AMA - RIKEN AIP Joint Workshop on Optimization and Machine Learning, Hong Kong Polytechnic University, May 2019.

7. RIKEN AIP Seminar, Tokyo, Japan, February 2018.

8. Advanced Optimization Seminar, McMaster University, May 2017. 

9. Workshop on Advances in Optimization, Tokyo, Japan, August 2016.

10. INFORMS Annual Meeting, Philadelphia, USA, November 2015.

11. Paths, Pivots, and Practice: The Power of Optimization, HEC Montréal, June 2015.

12. Canadian Mathematical Society Winter Meeting, Hamilton, Canada, December 2014.

Last updated: June 17, 2023