PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. - Paavo Parmas; Carl Edward Rasmussen; Jan Peters 001; Kenji Doya (ICML, 2018)

Total stochastic gradient algorithms and applications in reinforcement learning. - Paavo Parmas (NeurIPS, 2018)

A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme. - Paavo Parmas; Masashi Sugiyama (CoRR, 2019)

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. - Daniel Hennes; Dustin Morrill; Shayegan Omidshafiei; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Paavo Parmas; Edgar A. Duéñez-Guzmán; Karl Tuyls (AAMAS, 2020)

A unified view of likelihood ratio and reparameterization gradients. - Paavo Parmas; Masashi Sugiyama (AISTATS, 2021)

Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms. - Paavo Parmas; Takuma Seno (NeurIPS, 2022)

Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators. - Paavo Parmas; Takuma Seno; Yuma Aoki (ICML, 2023)

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form. - Toshinori Kitamura; Tadashi Kozuno; Wataru Kumagai; Kenta Hoshino; Yohei Hosoe; Kazumi Kasaura; Masashi Hamaya; Paavo Parmas; Yutaka Matsuo (ICLR, 2025)

Double Horizon Model-Based Policy Optimization. - Akihiro Kubo; Paavo Parmas; Shin Ishii (Trans. Mach. Learn. Res., 2025)