Nicholay Topin

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 28

Top Topics

Complex Domains

Design Patterns

Reinforcement Learning

Information Gathering

Top Venues

NeurIPS (Competition and Demos)

Publications

Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
Explainable Reinforcement Learning: A Survey and Comparative Review. ACM Comput. Surv. 56 (7) (2024)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. 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)
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)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS (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, 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)
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)
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)
Nicholay Topin, Manuela Veloso
Generation of Policy-Level Explanations for Reinforcement Learning. AAAI (2019)
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)
William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov
MineRL: A Large-Scale Dataset of Minecraft Demonstrations. CoRR (2019)
William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov
MineRL: A Large-Scale Dataset of Minecraft Demonstrations. IJCAI (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)
Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. CoRR (2019)
Nicholay Topin, Manuela Veloso
Generation of Policy-Level Explanations for Reinforcement Learning. CoRR (2019)
Leslie N. Smith, Nicholay Topin
Exploring loss function topology with cyclical learning rates. CoRR (2017)
Leslie N. Smith, Nicholay Topin
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. CoRR (2017)
Leslie N. Smith, Nicholay Topin
Deep Convolutional Neural Network Design Patterns. CoRR (2016)
Nicholay Topin, Karan K. Budhraja, Tim Oates
Feature Selection in Environments with Limited Voluntary Information Sharing. ICDM Workshops (2016)
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)
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)