Measuring and avoiding side effects using relative reachability. Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg. June 2018. (arXiv, blog post, code)
Reinforcement Learning with a Corrupted Reward Channel. Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg. IJCAI AI and Autonomy track, May 2017. (arXiv, demo, code)
Building Interpretable Models: From Bayesian Networks to Neural Networks. Viktoriya Krakovna (PhD thesis). September 2016.
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models. Viktoriya Krakovna, Finale Doshi-Velez.
- International Conference on Machine Learning (ICML) Workshop on Human Interpretability in Machine Learning (WHI), June 2016 (arXiv).
- Neural Information Processing Systems (NIPS) Workshop on Intepretable Machine Learning for Complex Systems, Dec 2016 (arXiv, poster).
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests. Viktoriya Krakovna, Chenguang Dai, Jun S. Liu. Statistics and Its Interface, Volume 11 Number 3, September 2018 (arXiv (older version), R package, code)
A Minimalistic Approach to Sum-Product Network Learning for Real Applications. Viktoriya Krakovna, Moshe Looks. International Conference for Learning Representations (ICLR) workshop track, May 2016. (arXiv, OpenReview, poster)
A generalized-zero-preserving method for compact encoding of concept lattices. Matthew Skala, Victoria Krakovna, Janos Kramar, Gerald Penn. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1512–1521, Uppsala, Sweden, July 2010.