Nicholay Topin

According to our database, Nicholay Topin authored at least 23 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Explainable Reinforcement Learning: A Survey and Comparative Review.

Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang

ACM Comput. Surv., July, 2024

2022

A Survey of Explainable Reinforcement Learning.

Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang

CoRR, 2022

MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning.

Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Use-Case-Grounded Simulations for Explanation Evaluation.

Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The MineRL BASALT Competition on Learning from Human Feedback.

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

CoRR, 2021

Towards robust and domain agnostic reinforcement learning competitions.

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

CoRR, 2021

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors.

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

CoRR, 2021

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned.

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

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods.

Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Guaranteeing Reproducibility in Deep Learning Competitions.

Brandon Houghton, Stephanie Milani, Nicholay Topin, William H. Guss, Katja Hofmann, Diego Perez Liebana, Manuela Veloso, Ruslan Salakhutdinov

CoRR, 2020

Towards robust and domain agnostic reinforcement learning competitions: MineRL 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

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

2019

Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy.

Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso

CoRR, 2019

The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors.

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

CoRR, 2019

Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning.

Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno

Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019

Online Planning for Autonomous Underwater Vehicles Performing Information Gathering Tasks in Large Subsea Environments.

Harun Yetkin, James McMahon, Nicholay Topin, Artur Wolek, Zachary Waters, Daniel J. Stilwell

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

MineRL: A Large-Scale Dataset of Minecraft Demonstrations.

William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden R. Codel, Manuela Veloso, Ruslan Salakhutdinov

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Generation of Policy-Level Explanations for Reinforcement Learning.

Nicholay Topin, Manuela Veloso

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2017

Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates.

Leslie N. Smith, Nicholay Topin

CoRR, 2017

Exploring loss function topology with cyclical learning rates.

Leslie N. Smith, Nicholay Topin

CoRR, 2017

2016

Deep Convolutional Neural Network Design Patterns.

Leslie N. Smith, Nicholay Topin

CoRR, 2016

Feature Selection in Environments with Limited Voluntary Information Sharing.

Nicholay Topin, Karan K. Budhraja, Tim Oates

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015

Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes.

Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014

Discovering Subgoals in Complex Domains.

Marie desJardins, Tenji Tembo, Nicholay Topin, Michael Bishoff, Shawn Squire, James MacGlashan, Rose Carignan, Nicholas Haltmeyer

Proceedings of the 2014 AAAI Fall Symposia, Arlington, Virginia, USA, November 13-15, 2014, 2014