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ORCIDNicholay Topin
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2020 – today
- 2024
[j1]Stephanie Milani
, Nicholay Topin, Manuela Veloso, Fei Fang:
Explainable Reinforcement Learning: A Survey and Comparative Review. ACM Comput. Surv. 56(7): 168:1-168:36 (2024)- 2022
- [c12]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS 2022 - [c11]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: 251-266 - [i17]Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang:
A Survey of Explainable Reinforcement Learning. CoRR abs/2202.08434 (2022) - [i16]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) - [i15]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 abs/2205.12449 (2022) - [i14]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. CoRR abs/2206.02256 (2022) - 2021
- [c10]Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso:
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. AAAI 2021: 9923-9931 - [c9]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 - [i13]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) - [i12]Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso:
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. CoRR abs/2102.13045 (2021) - [i11]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) - [i10]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
- [c8]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 - [i9]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) - [i8]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
- [c7]Nicholay Topin, Manuela Veloso:
Generation of Policy-Level Explanations for Reinforcement Learning. AAAI 2019: 2514-2521 - [c6]William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden R. Codel, Manuela Veloso, Ruslan Salakhutdinov:
MineRL: A Large-Scale Dataset of Minecraft Demonstrations. IJCAI 2019: 2442-2448 - [c5]Harun Yetkin, James McMahon, Nicholay Topin, Artur Wolek, Zachary Waters, Daniel J. Stilwell:
Online Planning for Autonomous Underwater Vehicles Performing Information Gathering Tasks in Large Subsea Environments. IROS 2019: 6354-6361 - [c4]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 - [i7]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) - [i6]Nicholay Topin, Manuela Veloso:
Generation of Policy-Level Explanations for Reinforcement Learning. CoRR abs/1905.12044 (2019) - [i5]Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso:
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. CoRR abs/1907.01180 (2019) - [i4]William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden R. Codel, Manuela Veloso, Ruslan Salakhutdinov:
MineRL: A Large-Scale Dataset of Minecraft Demonstrations. CoRR abs/1907.13440 (2019) - 2017
- [i3]Leslie N. Smith, Nicholay Topin:
Exploring loss function topology with cyclical learning rates. CoRR abs/1702.04283 (2017) - [i2]Leslie N. Smith, Nicholay Topin:
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. CoRR abs/1708.07120 (2017) - 2016
- [c3]Nicholay Topin, Karan K. Budhraja, Tim Oates:
Feature Selection in Environments with Limited Voluntary Information Sharing. ICDM Workshops 2016: 576-583 - [i1]Leslie N. Smith, Nicholay Topin:
Deep Convolutional Neural Network Design Patterns. CoRR abs/1611.00847 (2016) - 2015
- [c2]Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan:
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes. IJCAI 2015: 3856-3864 - 2014
- [c1]Marie desJardins, Tenji Tembo, Nicholay Topin, Michael Bishoff, Shawn Squire, James MacGlashan, Rose Carignan, Nicholas Haltmeyer:
Discovering Subgoals in Complex Domains. AAAI Fall Symposia 2014
Coauthor Index
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last updated on 2024-09-27 00:34 CEST by the dblp team
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