Stephanie Milani

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 38

Top Topics

Reinforcement Learning

Exploratory Analysis

Sequential Decision Problems

Top Venues

NeurIPS (Competition and Demos)

Publications

Ruiyi Wang, Stephanie Milani, Jamie C. Chiu, Shaun M. Eack, Travis Labrum, Samuel M. Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei Fang, Zhiyu Zoey Chen
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals. CoRR (2024)
Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
Explainable Reinforcement Learning: A Survey and Comparative Review. ACM Comput. Surv. 56 (7) (2024)
Raja Farrukh Ali, Stephanie Milani, John Woods, Emmanuel Adenij, Ayesha Farooq, Clayton Mansel, Jeffrey Burns, William H. Hsu
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction. CoRR (2024)
Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning. AAMAS (2024)
Zhuorui Ye, Stephanie Milani, Geoffrey J. Gordon, Fei Fang
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels. CoRR (2024)
Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake A. Richards, Irina Rish, Özgür Simsek
Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent. CoRR (2024)
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Sharada P. Mohanty, Byron Galbraith, Ke Chen, Yan Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv, Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik, Shu Ishida, João F. Henriques, Robert Klassert, Walter Laurito, Ellen R. Novoseller, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins, Josh Miller, Rohin Shah
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition. CoRR (2023)
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CHI (2023)
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Rohin Shah
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks. NeurIPS (2023)
Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran
Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning. CoRR (2023)
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Rohin Shah
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks. CoRR (2023)
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CoRR (2023)
Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca D. Dragan, Sam Devlin
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers. CoRR (2022)
Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca D. Dragan, Sam Devlin
UniMASK: Unified Inference in Sequential Decision Problems. CoRR (2022)
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning. ECML/PKDD (4) (2022)
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. CoRR (2022)
Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Jun Shern Chan, Daniel del Castillo, Tom Lieberum
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback. CoRR (2022)
Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin
Uni[MASK]: Unified Inference in Sequential Decision Problems. NeurIPS (2022)
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning. CoRR (2022)
Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
A Survey of Explainable Reinforcement Learning. CoRR (2022)
Evelyn Zuniga, Stephanie Milani, Guy Leroy, Jaroslaw Rzepecki, Raluca Georgescu, Ida Momennejad, David Bignell, Mingfei Sun, Alison Shaw, Gavin Costello, Mikhail Jacob, Sam Devlin, Katja Hofmann
How Humans Perceive Human-like Behavior in Video Game Navigation. CHI Extended Abstracts (2022)
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. NeurIPS (Competition and Demos) (2021)
William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada P. Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR (2021)
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada P. Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge J. 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
Towards robust and domain agnostic reinforcement learning competitions. CoRR (2021)
Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Jun Shern Chan, Daniel del Castillo, Tom Lieberum
Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback. NeurIPS (Competition and Demos) (2021)
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca D. Dragan
The MineRL BASALT Competition on Learning from Human Feedback. CoRR (2021)
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. CoRR (2021)
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. AAAI (2021)
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Sharada P. Mohanty, Byron Galbraith, Ke Chen, Yan Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv, Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik, Shu Ishida, João F. Henriques, Robert Klassert, Walter Laurito, Lucas Cazzonelli, Cedric Kulbach, Nicholas Popovic, Marvin Schweizer, Ellen R. Novoseller, Vinicius G. Goecks, Nicholas R. Waytowich, David Watkins, Josh Miller, Rohin Shah
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition. NeurIPS (Competition and Demos) (2021)
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
Planning with Abstract Learned Models While Learning Transferable Subtasks. AAAI (2020)
Brandon Houghton, Stephanie Milani, Nicholay Topin, William H. Guss, Katja Hofmann, Diego Perez Liebana, Manuela Veloso, Ruslan Salakhutdinov
Guaranteeing Reproducibility in Deep Learning Competitions. CoRR (2020)
Stephanie Milani, Weiran Shen, Kevin S. Chan, Sridhar Venkatesan, Nandi O. Leslie, Charles A. Kamhoua, Fei Fang
Harnessing the Power of Deception in Attack Graph-Based Security Games. GameSec (2020)
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada P. Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge J. 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
Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020. NeurIPS (Competition and Demos) (2020)
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning. CoRR (2020)
Huao Li, Stephanie Milani, Vigneshram Krishnamoorthy, Michael Lewis, Katia P. Sycara
Perceptions of Domestic Robots' Normative Behavior Across Cultures. AIES (2019)
William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noburu Kuno, Stephanie Milani, Sharada P. Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang
The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR (2019)
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning. NeurIPS (Competition and Demos) (2019)
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
Planning with Abstract Learned Models While Learning Transferable Subtasks. CoRR (2019)