Ya Ping Hsieh (2020)

Research Interests:

  • Statistical Learning Theory
  • Convex/Non-Convex Optimization
  • Concentration of Measure Phenomenon


I received my B.S.E. degree in Electrical Engineering in 2010, and an M.S. degree in Communication Engineering in 2012, both from National Taiwan University. I was a research assistant at the Research Centerfor Information Technology Innovation, Academia Sinica, from 2013 to 2014. I am broadly interested in any theory regarding data analysis, including statistical learning and convex optimization. I also enjoy browsing several branches of mathematics, including the information theory and concentration of measure phenomenon. In 2015 I started my PhD which I completed in 2020. The thesis, entitled “Convergence without convexity: sampling, optimization, and games” was supervised by Prof. Volkan Cevher.

Publications (most recent)

The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets

Y-P. Hsieh; P. Mertikopoulos; V. Cevher 

2021. 38th International Conference on Machine Learning (ICML 2021), Online, July 18-24, 2021. p. 4337-4348.

Convergence without Convexity: Sampling, Optimization, and Games

Y-P. Hsieh / V. Cevher (Dir.)  

Lausanne, EPFL, 2020. 

Conditional gradient methods for stochastically constrained convex minimization

M-L. Vladarean; A. Alacaoglu; Y-P. Hsieh; V. Cevher 

2020. 37th International Conference on Machine Learning (ICML), virtual, July 12-18, 2020.

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

K. Parameswaran; Y-T. Huang; Y-P. Hsieh; P. T. Y. Rolland; C. Shi et al. 


Finding Mixed Nash Equilibria of Generative Adversarial Networks

Y-P. Hsieh; C. Liu; V. Cevher 

2018. IEEE International Conference on Machine Learning (ICML)’ 2019, Long Beach, USA, June 9-15, 2019.

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