Nicholay Topin

31 papers A* 5A 1Journal 18Unranked 6

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Explainable Reinforcement Learning: A Survey and Comparative Review. ACM Comput. Surv. Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
2022	J	`jnl`	A Survey of Explainable Reinforcement Learning. CoRR Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
2022	J	`jnl`	MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning. CoRR Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang
2022	—	`conf`	MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning. ECML/PKDD (4) Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang
2022	J	`jnl`	MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. CoRR 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
2022	—	`—`	Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin
2022	A*	`conf`	Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
2022	J	`jnl`	Use-Case-Grounded Simulations for Explanation Evaluation. CoRR Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
2021	A*	`conf`	Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. AAAI Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
2021	J	`jnl`	Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. CoRR Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
2021	—	`conf`	MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. NeurIPS (Competition and Demos) 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
2021	J	`jnl`	The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR 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
2021	J	`jnl`	The MineRL BASALT Competition on Learning from Human Feedback. CoRR 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
2021	J	`jnl`	Towards robust and domain agnostic reinforcement learning competitions. CoRR 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
2020	J	`jnl`	Guaranteeing Reproducibility in Deep Learning Competitions. CoRR Brandon Houghton, Stephanie Milani, Nicholay Topin, William H. Guss, Katja Hofmann, Diego Perez Liebana, Manuela Veloso, Ruslan Salakhutdinov
2020	J	`jnl`	Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning. CoRR Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno
2020	—	`conf`	Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020. NeurIPS (Competition and Demos) 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
2019	J	`jnl`	Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. CoRR Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso
2019	A*	`conf`	Generation of Policy-Level Explanations for Reinforcement Learning. AAAI Nicholay Topin, Manuela Veloso
2019	J	`jnl`	Generation of Policy-Level Explanations for Reinforcement Learning. CoRR Nicholay Topin, Manuela Veloso
2019	A*	`conf`	MineRL: A Large-Scale Dataset of Minecraft Demonstrations. IJCAI William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden R. Codel, Manuela Veloso, Ruslan Salakhutdinov
2019	J	`jnl`	MineRL: A Large-Scale Dataset of Minecraft Demonstrations. CoRR William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden R. Codel, Manuela Veloso, Ruslan Salakhutdinov
2019	A	`conf`	Online Planning for Autonomous Underwater Vehicles Performing Information Gathering Tasks in Large Subsea Environments. IROS Harun Yetkin, James McMahon, Nicholay Topin, Artur Wolek, Zachary Waters, Daniel J. Stilwell
2019	—	`conf`	Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning. NeurIPS (Competition and Demos) Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno
2019	J	`jnl`	The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR 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
2017	J	`jnl`	Exploring loss function topology with cyclical learning rates. CoRR Leslie N. Smith, Nicholay Topin
2017	J	`jnl`	Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. CoRR Leslie N. Smith, Nicholay Topin
2016	J	`jnl`	Deep Convolutional Neural Network Design Patterns. CoRR Leslie N. Smith, Nicholay Topin
2016	—	`conf`	Feature Selection in Environments with Limited Voluntary Information Sharing. ICDM Workshops Nicholay Topin, Karan K. Budhraja, Tim Oates
2015	A*	`conf`	Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes. IJCAI Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan
2014	—	`conf`	Discovering Subgoals in Complex Domains. AAAI Fall Symposia Marie desJardins, Tenji Tembo, Nicholay Topin, Michael Bishoff, Shawn Squire, James MacGlashan, Rose Carignan, Nicholas Haltmeyer