Stephanie Milani

I am a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, where I am advised by Fei Fang. I aim to create intelligent agents that can learn quickly, explain their decisions, and work harmoniously with people and other artificially intelligent agents. I am particularly interested in reinforcement learning.

I graduated in 2019 with a B.S. in Computer Science and a B.A. in Psychology from the University of Maryland, Baltimore County. There, I was advised by Marie desJardins and Cynthia Matuszek. I have also had the pleasure of working with Jennifer Wenzel, Christoph Mertz, Katia Sycara, and David Held.

I am open to and excited about collaborating with others. Please email me if you have any questions about a machine learning (or psychology) project, or if you are interested in a research collaboration.

Email  /  CV  /  Google Scholar  /  LinkedIn  /  Twitter

Refereed Publications

Understanding Human-like Behavior in Video Game Navigation
Evelyn Zuniga*, Stephanie Milani*, Mikhail Jacob, Katja Hofmann
NeurIPS Workshop on Human-Centered AI, 2021

The MineRL Diamond Competition on Sample Efficient Reinforcement Learning
William H. Guss, Alara Dirik*, Byron Galbraith*, Brandon Houghton*, Anssi Kanervisto*, Noboru Sean Kuno, Stephanie Milani*, Sharada Mohanty*, Karolis Ramanauskas*, Ruslan Salakhutdinov*, Rohin Shah*, Nicholay Topin*, Steven H. Wang*, Cody Wild*
NeurIPS Competition Track, 2021

The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan
NeurIPS Competition Track, 2021

Towards Robust and Domain Agnostic Reinforcement Learning Competitions
William H. Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie Wu, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute
Proceedings of the NeurIPS 2020 Competition & Demonstration Track, 2021

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
AAAI, 2021
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Harnessing the Power of Deception in Attack Graph-Based Security Games
Stephanie Milani, Weiran Shen, Kevin S. Chan, Sridhar Venkatesan, Nandi O. Leslie, Charles Kamhoua, Fei Fang
GameSec, 2020
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A Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno
Proceedings of the NeurIPS 2019 Competition & Demonstration Track, 2020
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NeurIPS2020 Competition: The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Mario Ynocente Castro*, Sam Devlin*, Brandon Houghton*, Noboru Sean Kuno*, Crissman Loomis*, Stephanie Milani*, Sharada Mohanty*, Keisuke Nakata*, Ruslan Salakhutdinov*, John Schulman*, Shinya Shiroshita*, Nicholay Topin*, Avinash Ummadisingu*, Oriol Vinyals*
NeurIPS Competition Track, 2020
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Planning with Abstract, Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
AAAI, 2020
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Intelligent Tutoring Strategies for Students with Autism Spectrum Disorder: A Reinforcement Learning Approach.
Stephanie Milani*, Zhou Fan*, Saurabh Gulati, Thanh Nguyen, Fei Fang, Amulya Yadav
AAAI Workshop on AI for Education, 2020
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The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Cayden Codel*, Katja Hofmann*, Brandon Houghton*, Noboru Sean Kuno*, Stephanie Milani*, Sharada Mohanty*, Diego Perez-Liebana*, Ruslan Salakhutdinov*, Nicholay Topin*, Manuela Veloso*, Phillip Wang*
NeurIPS Competition Track, 2019
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Guaranteeing Reproducibility in Deep Learning Competitions
Brandon Houghton, Stephanie Milani, Nicholay Topin, William H. Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov
NeurIPS CiML Workshop, 2019
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Planning with Abstract, Learned Models
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shawn Squire, Marie desJardins, Cynthia Matuszek
Do Good Robotics Symposium, 2019
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Penalty-Modified Markov Decision Processes: Efficient Incorporation of Norms into Sequential Decision Making Problems
Stephanie Milani, Nicholay Topin, Katia Sycara
RLDM, 2019
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Perceptions of Domestic Robots' Normative Behavior Across Cultures
Huao Li, Stephanie Milani, Vigneshram Krishnamoorthy, Michael Lewis, Katia Sycara
AI, Ethics, and Society, 2019
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Towards Planning with Hierarchies of Learned Markov Decision Processes
John Winder, Shawn Squire, Matthew Landen, Stephanie Milani, Marie desJardins
ICAPS Workshop on Integrated Planning, Acting, and Execution, 2017
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R-AMDP: Model-Based Learning for Abstract Markov Decision Process Hierarchies
Shawn Squire, John Winder, Matthew Landen, Stephanie Milani, Marie desJardins
RLDM, 2017
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I got this great website here.