Discovering Subgoals in Complex Domains. - Marie desJardins; Tenji Tembo; Nicholay Topin; Michael Bishoff; Shawn Squire; James MacGlashan; Rose Carignan; Nicholas Haltmeyer (AAAI Fall Symposia, 2014)

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 (IJCAI, 2015)

Feature Selection in Environments with Limited Voluntary Information Sharing. - Nicholay Topin; Karan K. Budhraja; Tim Oates 001 (ICDM Workshops, 2016)

Deep Convolutional Neural Network Design Patterns. - Leslie N. Smith; Nicholay Topin (CoRR, 2016)

Exploring loss function topology with cyclical learning rates. - Leslie N. Smith; Nicholay Topin (CoRR, 2017)

Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. - Leslie N. Smith; Nicholay Topin (CoRR, 2017)

Generation of Policy-Level Explanations for Reinforcement Learning. - Nicholay Topin; Manuela Veloso (AAAI, 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 (IJCAI, 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 (IROS, 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 (NeurIPS (Competition and Demos), 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)

Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. - Aaron M. Roth; Nicholay Topin; Pooyan Jamshidi; Manuela Veloso (CoRR, 2019)

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; et al. (NeurIPS (Competition and Demos), 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)

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods. - Nicholay Topin; Stephanie Milani; Fei Fang 001; Manuela Veloso (AAAI, 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 016; Wei Yang 032; Weijun Hong; Zhongyue Huang; et al. (NeurIPS (Competition and Demos), 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; et al. (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; et al. (CoRR, 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 001; et al. (CoRR, 2021)

Use-Case-Grounded Simulations for Explanation Evaluation. - Valerie Chen; Nari Johnson; Nicholay Topin; Gregory Plumb; Ameet Talwalkar (NeurIPS, 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 001 (ECML/PKDD (4), 2022)

A Survey of Explainable Reinforcement Learning. - Stephanie Milani; Nicholay Topin; Manuela Veloso; Fei Fang 001 (CoRR, 2022)

Unifying State and Policy-Level Explanations for Reinforcement Learning - Nicholay Topin (2022)

Explainable Reinforcement Learning: A Survey and Comparative Review. - Stephanie Milani; Nicholay Topin; Manuela Veloso; Fei Fang 001 (ACM Comput. Surv., 2024)