About Me

PhD student working on computational aspects of differential, geometric, and algebraic structures (i.e., probability distributions and matrices). My research so far has mostly focused on geometric methods for approximate inference and numerical optimization in machine learning.

For natural-gradient (NG) methods, please see

  • Structured NG descent (ICML 2021): Long Talk, Short Talk, Paper, Blog
  • Riemannian gradient descent (ICML 2020): Talk, Paper

  • NG descent for exponential-family mixtures (ICML 2019): Paper
  • NG descent for Bayesian deep learninng (ICML 2018): Paper
  • NG variational inference for non-conjugate models (AI&Stats 2017): Paper

For an introduction to NG methods, see my Blog.

For more publications, see my Google Scholar page.

I review papers from conferences (ICML, NeurIPS, ICLR, AI&Stats), journals (JMLR, Information Geometry (Springer)), and workshops (Optimization for Machine Learning, Advances in Approximate Bayesian Inference).

Research Interests

I am interested in exploiting (hidden) structures and symmetries in machine learning with a focus on practical and numerical methods for optimization and statistical inference.


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