Alexander Tamas Postdoc in Artifical Intelligence, University of Oxford
I work on AI Safety and Reinforcement Learning at the Future of Humanity Institute (directed by Nick Bostrom). I also lead a project on "Inferring Human Preferences" with Andreas Stuhlmueller of Stanford University. My PhD is from MIT, where I worked on cognitive science, probabilistic programming, and philosophy of science.
If you'd like to do an internship with me, send your CV and statement of interest here.
My name is pronounced "O-wine".
- Evans O., Stuhlmueller A., Salvatier J., Filan D. (2017). Agentmodels.org: Modeling Agents with Probabilistic Programs.. Interactive Online Book
- Abel D., Salvatier J., Stuhlmueller A., Evans O. (2016). Agent-Agnostic Human-in-the-Loop Reinforcement Learning. NIPS Workshop.
- Krueger D., Leike J, Salvatier J., Evans O. (2016). Active Reinforcement Learning: Observing Rewards at a Cost. NIPS Workshop.
- Evans O., Stuhlmueller A., Goodman N. (2016). Learning the Preferences of Ignorant, Inconsistent Agents. AAAI
- Evans O., Stuhlmueller A., Goodman N. (2015). Learning the Preferences of Bounded Agents. NIPS Workshop.
- Evans O., Bergen L., Tenenbaum J. (2012). Learning Structured Preferences. Cogsci
- Ullman T., Baker C., Macindoe O., Evans O., Goodman N., & Tenenbaum J. (2010). Help or hinder: Bayesian models of social goal inference. NIPS
"Bayesian Computational Models for Inferring Preferences" (2015). MIT Dissertation
- Model Mis-specification and Inverse Reinforcement Learning. (Blogpost co-authored with Jacob Steinhardt).
- Automated Corporations and AI Risk (Informal talk at Oxford University)
- Agent-agnostic Human-in-the-loop Reinforcement Learning (Slides for talks at U. Toronto and Deepmind)
- Learning the Preferences of Ignorant, Inconsistent Agents (Slides for oral presentation at AAAI 2016)
- Learning Human Preferences (Short talk at MIT)
Adapted from Matei Zaharia and Andreas Viklund.