Ivan Ovinnikov
Ivan Ovinnikov

Researcher

ETH Zürich

About Me

I am a researcher in reinforcement learning and generative modeling recently graduated with a doctoral degree from ETH Zürich.

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Interests
  • Artificial Intelligence
  • Reinforcement Learning
  • Generative Modeling
Education

  • PhD Computer Science (ML/AI)

    ETH Zürich Department of Computer Science

  • MSc Electrical Engineering (Intelligent Systems)

    ETH Zürich Department of Electrical Engineering

  • BSc Electrical Engineering

    ETH Zürich Department of Electrical Engineering

Recent Publications
Ivan Ovinnikov, Ami Beuret, Flavia Cavaliere, Joachim M Buhmann (2024). Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark. International Journal of Computer Assisted Radiology and Surgery.
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Ivan Ovinnikov, Eugene Bykovets, Joachim M. Buhmann (2024). Learning Causally Invariant Reward Functions from Diverse Demonstrations.
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Ivan Ovinnikov, Joachim M. Buhmann (2023). Regularizing Adversarial Imitation Learning Using Causal Invariance.
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Luis Haug, Ivan Ovinnikov, Eugene Bykovets (2020). Inverse Reinforcement Learning via Matching of Optimality Profiles.
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Ivan Ovinnikov (2020). Poincare Wasserstein Autoencoder.
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Recent & Upcoming Talks
PhD Examination
Recent News

PhD examination

Successfully passed my PhD examination

Teaching

I have been part of a number of courses over the years as a teaching assistant:

  • Advanced Machine Learning (ETH Zürich, 2020, 2021, 2023)
  • Statistical Learning Theory (ETH Zürich, 2020, 2021, 2024)
  • Algorithmic Game Theory (ETH Zürich, 2019)
  • Linear Algebra (ETH Zürich, 2017)
  • Informatik I (ETH Zürich, 2017)

I have also supervised a number of student theses:

  • Ege Karaismaloglu (Bachelor Thesis): Abstract Reasoning in Neural Networks
  • Riccardo De Santi (Research in CS Project): Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments
  • Marcus Vierneisel (Master Thesis): Exploration of Latent Space in Adversarial Imitation Learning
  • Daniel Garellick (Semester Thesis): Reducing Spurious Correlations in Better-than-Demonstrator Imitation Learning
  • Vitaly Dmitriev (MAS): Applications of Regularized Classification Methods to Surgical Skill Assessment
  • Shengdi Chen (Semester Thesis): An Empirical Study of Primal Wasserstein Imitation Learning in Goal-Conditioned RL