DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets. - Claas Völcker; Alejandro Molina 001; Johannes Neumann; Dirk Westermann; Kristian Kersting (PKDD/ECML Workshops (1), 2019)

Structured Object-Aware Physics Prediction for Video Modeling and Planning. - Jannik Kossen; Karl Stelzner; Marcel Hussing; Claas Voelcker; Kristian Kersting (ICLR, 2020)

Value Gradient weighted Model-Based Reinforcement Learning. - Claas Voelcker; Victor Liao; Animesh Garg; Amir-massoud Farahmand (ICLR, 2022)

Queer In AI: A Case Study in Community-Led Participatory AI. - Organizers Of QueerInAI; Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; et al. (FAccT, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; Jaidev Shriram; et al. (CoRR, 2023)

λ-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces. - Claas Voelcker; Arash Ahmadian; Romina Abachi; Igor Gilitschenski; Amir-massoud Farahmand (CoRR, 2023)

Temporal-Difference Learning Using Distributed Error Signals. - Jonas Guan; Shon Eduard Verch; Claas Voelcker; Ethan C. Jackson; Nicolas Papernot; William A. Cunningham (NeurIPS, 2024)

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence. - Marcel Hussing; Claas Voelcker; Igor Gilitschenski; Amir-massoud Farahmand; Eric Eaton (RLJ, 2024)

When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning. - Claas Voelcker; Tyler Kastner; Igor Gilitschenski; Amir-massoud Farahmand (RLJ, 2024)

Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence. - Marcel Hussing; Claas Voelcker; Igor Gilitschenski; Amir-massoud Farahmand; Eric Eaton (CoRR, 2024)

Can we hop in general? A discussion of benchmark selection and design using the Hopper environment. - Claas Völcker; Marcel Hussing; Eric Eaton (CoRR, 2024)

MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL. - Claas Voelcker; Marcel Hussing; Eric Eaton; Amir-massoud Farahmand; Igor Gilitschenski (ICLR, 2025)

Calibrated Value-Aware Model Learning with Stochastic Environment Models. - Claas Voelcker; Anastasiia Pedan; Arash Ahmadian; Romina Abachi; Igor Gilitschenski; Amir-massoud Farahmand (CoRR, 2025)

Sorrel: A simple and flexible framework for multi-agent reinforcement learning. - Rebekah A. Gelpí; Yibing Ju; Ethan C. Jackson; Yikai Tang; Shon Eduard Verch; Claas Voelcker; William A. Cunningham (CoRR, 2025)

Relative Entropy Pathwise Policy Optimization. - Claas Voelcker; Axel Brunnbauer; Marcel Hussing; Michal Nauman; Pieter Abbeel; Eric Eaton; Radu Grosu; Amir-massoud Farahmand; Igor Gilitschenski (CoRR, 2025)