Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments. - John D. Martin; Brendan J. Englot (CoRL, 2017)

Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation. - John D. Martin; Jinkun Wang; Brendan J. Englot (CoRL, 2018)

Recursive Sparse Pseudo-input Gaussian Process SARSA. - John D. Martin; Brendan J. Englot (CoRR, 2018)

Stochastically Dominant Distributional Reinforcement Learning. - John D. Martin; Michal Lyskawinski; Xiaohu Li; Brendan J. Englot (ICML, 2020)

Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs. - Fanfei Chen; John D. Martin; Yewei Huang; Jinkun Wang; Brendan J. Englot (IROS, 2020)

Variational Filtering with Copula Models for SLAM. - John D. Martin; Kevin J. Doherty 001; Caralyn Cyr; Brendan J. Englot; John J. Leonard (IROS, 2020)

Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction. - John McConnell; John D. Martin; Brendan J. Englot (IROS, 2020)

On Catastrophic Interference in Atari 2600 Games. - William Fedus; Dibya Ghosh; John D. Martin; Marc G. Bellemare; Yoshua Bengio; Hugo Larochelle (CoRR, 2020)

Adapting the Function Approximation Architecture in Online Reinforcement Learning. - John D. Martin; Joseph Modayil (CoRR, 2021)

Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant. - Joseph R. Larsen; Margaret R. Martin; John D. Martin; James B. Hicks; Peter Kuhn (PLoS Comput. Biol., 2021)

Should Models Be Accurate? - Esra'a Saleh; John D. Martin; Anna Koop; Arash Pourzarabi; Michael Bowling (CoRR, 2022)

MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning. - Rafael Rafailov; Kyle Beltran Hatch; Victor Kolev; John D. Martin; Mariano Phielipp; Chelsea Finn (CoRL, 2023)

Settling the Reward Hypothesis. - Michael Bowling; John D. Martin; David Abel; Will Dabney (ICML, 2023)

Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment. - Xi Lin 006; Paul Szenher; John D. Martin; Brendan J. Englot (CoRR, 2023)

Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning. - Bradley Burega; John D. Martin; Luke Kapeluck; Michael Bowling (CoRR, 2024)

Improved Silent Data Error Detection Through Test Optimization Using Reinforcement Learning. - Manu Shamsa; John D. Martin; Mariano Phielipp; Thiago Macieira; Loganathan Lingappan; Brad Kelly; David Lerner; Michael Tucknott; Ethan Hansen (IRPS, 2025)

On the Interplay Between Sparsity and Training in Deep Reinforcement Learning. - Fatima Davelouis; John D. Martin; Michael Bowling (CoRR, 2025)