Generating 3D Plants using Lindenmayer System. - Kamil Ciosek; Pawel Kotowski (GRAPP, 2009)

Compositional Planning Using Optimal Option Models. - David Silver; Kamil Ciosek (ICML, 2012)

Properties of the Least Squares Temporal Difference learning algorithm - Kamil Ciosek (CoRR, 2013)

Value Iteration with Options and State Aggregation. - Kamil Ciosek; David Silver (CoRR, 2015)

Linear reinforcement learning with options. - Kamil Andrzej Ciosek (2015)

Alternating Optimisation and Quadrature for Robust Reinforcement Learning. - Supratik Paul; Kamil Ciosek; Michael A. Osborne; Shimon Whiteson (CoRR, 2016)

OFFER: Off-Environment Reinforcement Learning. - Kamil Andrzej Ciosek; Shimon Whiteson (AAAI, 2017)

Expected Policy Gradients. - Kamil Ciosek; Shimon Whiteson (AAAI, 2018)

Alternating Optimisation and Quadrature for Robust Control. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (AAAI, 2018)

Fourier Policy Gradients. - Matthew Fellows; Kamil Ciosek; Shimon Whiteson (ICML, 2018)

Better Exploration with Optimistic Actor Critic. - Kamil Ciosek; Quan Vuong; Robert Tyler Loftin; Katja Hofmann (NeurIPS, 2019)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

Pre-training as Batch Meta Reinforcement Learning with tiMe. - Quan Vuong; Shuang Liu; Minghua Liu; Kamil Ciosek; Hao Su 001; Henrik Iskov Christensen (CoRR, 2019)

AMRL: Aggregated Memory For Reinforcement Learning. - Jacob Beck; Kamil Ciosek; Sam Devlin; Sebastian Tschiatschek; Cheng Zhang 005; Katja Hofmann (ICLR, 2020)

Conservative Uncertainty Estimation By Fitting Prior Networks. - Kamil Ciosek; Vincent Fortuin; Ryota Tomioka; Katja Hofmann; Richard E. Turner (ICLR, 2020)

Discount Factor as a Regularizer in Reinforcement Learning. - Ron Amit; Ron Meir; Kamil Ciosek (ICML, 2020)

Multi-task Batch Reinforcement Learning with Metric Learning. - Jiachen Li; Quan Vuong; Shuang Liu; Minghua Liu; Kamil Ciosek; Henrik I. Christensen; Hao Su 001 (NeurIPS, 2020)

DRIFT: Deep Reinforcement Learning for Functional Software Testing. - Luke Harries; Rebekah Storan Clarke; Timothy Chapman; Swamy V. P. L. N. Nallamalli; Levent Özgür; Shuktika Jain; Alex Leung; Steve Lim; Aaron Dietrich; José Miguel Hernández-Lobato; Tom Ellis; Cheng Zhang 005; et al. (CoRR, 2020)

Expected Policy Gradients for Reinforcement Learning. - Kamil Ciosek; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Estimating α-Rank by Maximizing Information Gain. - Tabish Rashid; Cheng Zhang 005; Kamil Ciosek (AAAI, 2021)

Regularized Policies are Reward Robust. - Hisham Husain; Kamil Ciosek; Ryota Tomioka (AISTATS, 2021)

Evaluating the Robustness of Collaborative Agents. - Paul A. Knott; Micah Carroll; Sam Devlin; Kamil Ciosek; Katja Hofmann; Anca D. Dragan; Rohin Shah (AAMAS, 2021)

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. - Luisa M. Zintgraf; Sam Devlin; Kamil Ciosek; Shimon Whiteson; Katja Hofmann (AAMAS, 2021)

Information Directed Reward Learning for Reinforcement Learning. - David Lindner; Matteo Turchetta; Sebastian Tschiatschek; Kamil Ciosek; Andreas Krause 001 (NeurIPS, 2021)

Imitation Learning by Reinforcement Learning. - Kamil Ciosek (ICLR, 2022)

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. - Thomas M. McDonald 001; Lucas Maystre; Mounia Lalmas; Daniel Russo 001; Kamil Ciosek (KDD, 2023)

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning. - Federico Tomasi; Joseph Cauteruccio; Surya Kanoria; Kamil Ciosek; Matteo Rinaldi; Zhenwen Dai (KDD, 2023)

A Strong Baseline for Batch Imitation Learning. - Matthew Smith; Lucas Maystre; Zhenwen Dai; Kamil Ciosek (CoRR, 2023)

Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel. - Sergio Calvo-Ordoñez; Konstantina Palla; Kamil Ciosek (CoRR, 2024)

On the Importance of Uncertainty in Decision-Making with Large Language Models. - Nicolò Felicioni; Lucas Maystre; Sina Ghiassian; Kamil Ciosek (Trans. Mach. Learn. Res., 2024)

Impatient Bandits: Optimizing for the Long-Term Without Delay. - Kelly W. Zhang; Thomas Baldwin-McDonald; Kamil Ciosek; Lucas Maystre; Daniel Russo 001 (CoRR, 2025)

Observation Noise and Initialization in Wide Neural Networks. - Sergio Calvo-Ordoñez; Jonathan Plenk; Richard Bergna; Alvaro Cartea; José Miguel Hernández-Lobato; Konstantina Palla; Kamil Ciosek (CoRR, 2025)

Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy. - Kamil Ciosek; Nicolò Felicioni; Sina Ghiassian (CoRR, 2025)