Deep Function Machines: Generalized Neural Networks for Topological Layer Expression. - William H. Guss (CoRR, 2016)

On Characterizing the Capacity of Neural Networks using Algebraic Topology. - William H. Guss; Ruslan Salakhutdinov (CoRR, 2018)

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)

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)

On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps. - William H. Guss; Ruslan Salakhutdinov (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)

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. - Sharada P. Mohanty; Jyotish Poonganam; Adrien Gaidon; Andrey Kolobov; Blake Wulfe; Dipam Chakraborty; Grazvydas Semetulskis; João Schapke; Jonas Kubilius; Jurgis Pasukonis; Linas Klimas; Matthew J. Hausknecht; 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)

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)

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft. - Ingmar Kanitscheider; Joost Huizinga; David Farhi; William Hebgen Guss; Brandon Houghton; Raul Sampedro; Peter Zhokhov; Bowen Baker; Adrien Ecoffet; Jie Tang; Oleg Klimov; Jeff Clune (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)

Evaluating Large Language Models Trained on Code. - Mark Chen 003; Jerry Tworek; Heewoo Jun; Qiming Yuan; Henrique Pondé de Oliveira Pinto; Jared Kaplan; Harri Edwards; Yuri Burda; Nicholas Joseph; Greg Brockman; Alex Ray; Raul Puri; et al. (CoRR, 2021)