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Stephanie Milani
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2020 – today
- 2024
- [j1]Stephanie Milani
, Nicholay Topin
Explainable Reinforcement Learning: A Survey and Comparative Review. ACM Comput. Surv. 56(7): 168:1-168:36 (2024) - [c17]Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran:
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning. AAMAS 2024: 1865-1873 - [c16]Ruiyi Wang, Stephanie Milani, Jamie C. Chiu, Jiayin Zhi, Shaun M. Eack, Travis Labrum, Samuel M. Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei Fang, Zhiyu Chen:
PATIENT-ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals. EMNLP 2024: 12772-12797 - [c15]My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri, Gokul Swamy, Geoffrey J. Gordon:
When is Transfer Learning Possible? ICML 2024 - [i22]Ruiyi Wang, Stephanie Milani, Jamie C. Chiu
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals. CoRR abs/2405.19660 (2024) - [i21]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 abs/2406.07777 (2024) - [i20]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 abs/2407.12161 (2024) - [i19]Zhuorui Ye, Stephanie Milani, Geoffrey J. Gordon, Fei Fang:
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels. CoRR abs/2407.15786 (2024) - 2023
- [c14]Stephanie Milani
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CHI 2023: 572:1-572:18 - [c13]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 - [i18]Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CoRR abs/2303.02160 (2023) - [i17]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 abs/2303.13512 (2023) - [i16]Aravind Venugopal, Stephanie Milani, Fei Fang
Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning. CoRR abs/2304.06011 (2023) - [i15]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 abs/2312.02405 (2023) - 2022
- [c12]Evelyn Zuniga, Stephanie Milani
How Humans Perceive Human-like Behavior in Video Game Navigation. CHI Extended Abstracts 2022: 391:1-391:11 - [c11]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 - [c10]Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning. ECML/PKDD (4) 2022: 251-266 - [i14]Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang:
A Survey of Explainable Reinforcement Learning. CoRR abs/2202.08434 (2022) - [i13]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 abs/2202.10583 (2022) - [i12]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 abs/2204.07123 (2022) - [i11]Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin:
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers. CoRR abs/2204.13326 (2022) - [i10]Stephanie Milani
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning. CoRR abs/2205.12449 (2022) - [i9]Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani
UniMASK: Unified Inference in Sequential Decision Problems. CoRR abs/2211.10869 (2022) - 2021
- [c9]Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso:
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. AAAI 2021: 9923-9931 - [c8]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: 13-28 - [c7]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: 171-188 - [c6]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: 259-272 - [i8]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 abs/2101.11071 (2021) - [i7]Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso:
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. CoRR abs/2102.13045 (2021) - [i6]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 abs/2106.03748 (2021) - [i5]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 abs/2107.01969 (2021) - 2020
- [c5]John Winder, Stephanie Milani, Matthew Landen, Erebus Oh
Planning with Abstract Learned Models While Learning Transferable Subtasks. AAAI 2020: 9992-10000 - [c4]Stephanie Milani
Harnessing the Power of Deception in Attack Graph-Based Security Games. GameSec 2020: 147-167 - [c3]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: 233-252 - [i4]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 abs/2003.05012 (2020) - [i3]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 abs/2005.06041 (2020)
2010 – 2019
- 2019
- [c2]Huao Li, Stephanie Milani
Perceptions of Domestic Robots' Normative Behavior Across Cultures. AIES 2019: 345-351 - [c1]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: 203-214 - [i2]William H. Guss, Cayden R. 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 abs/1904.10079 (2019) - [i1]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 abs/1912.07544 (2019)
Coauthor Index
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last updated on 2024-11-15 19:35 CET by the dblp team
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