@inproceedings{guans2024temporaldifference,title={Temporal-Difference Learning using Distributed Error Signals},author={Guan, Jonas and Verch, Shon Eduard and Voelcker, Claas A. and Jackson, Ethan C and Papernot, Nicolas and Cunningham, William A},booktitle={Neural Information Processing Systems},year={2024},url={https://openreview.net/forum?id=8motqjfqav},month=dec}
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
Claas A. Voelcker, Marcel Hussing, and Eric Eaton
In Finding the Frame: An RLC Workshop for Examining Conceptual Frameworks, Aug 2024
@inproceedings{voelcker2024can,title={Can we hop in general? A discussion of benchmark selection and design using the Hopper environment},author={Voelcker, Claas A. and Hussing, Marcel and Eaton, Eric},booktitle={Finding the Frame: An RLC Workshop for Examining Conceptual Frameworks},year={2024},url={https://openreview.net/forum?id=9IgtF63LPA},month=aug}
Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence
Marcel Hussing, Claas A. Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, and Eric Eaton
@article{hussing2024dissecting,title={Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence},author={Hussing, Marcel and Voelcker, Claas A. and Gilitschenski, Igor and Farahmand, Amir-massoud and Eaton, Eric},journal={Reinforcement Learning Conference},year={2024},month=aug}
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
Claas A. Voelcker, Tyler Kastner, Igor Gilitschenski, and Amir-massoud Farahmand
@article{voelcker2024does,title={When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning},author={Voelcker, Claas A. and Kastner, Tyler and Gilitschenski, Igor and Farahmand, Amir-massoud},journal={Reinforcement Learning Conference},year={2024},month=aug}
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas A. Voelcker, Marcel Hussing, Eric Eaton, and Igor Farahmand
@article{voelcker2024mad,title={MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL},author={Voelcker, Claas A. and Hussing, Marcel and Eaton, Eric and Farahmand, Amir-massoudand Gilitschenski, Igor},journal={under review},year={2024},month=oct,}
2023
Queer In AI: A Case Study in Community-Led Participatory AI
Organizers Of Queerinai, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas A. Voelcker, and 45 more authors
In Conference on Fairness, Accountability, and Transparency, Jan 2023
@inproceedings{10.1145/3593013.3594134,author={Queerinai, Organizers Of and Ovalle, Anaelia and Subramonian, Arjun and Singh, Ashwin and Voelcker, Claas A. and Sutherland, Danica J. and Locatelli, Davide and Breznik, Eva and Klubicka, Filip and Yuan, Hang and J, Hetvi and Zhang, Huan and Shriram, Jaidev and Lehman, Kruno and Soldaini, Luca and Sap, Maarten and Deisenroth, Marc Peter and Pacheco, Maria Leonor and Ryskina, Maria and Mundt, Martin and Agarwal, Milind and Mclean, Nyx and Xu, Pan and Pranav, A and Korpan, Raj and Ray, Ruchira and Mathew, Sarah and Arora, Sarthak and John, St and Anand, Tanvi and Agrawal, Vishakha and Agnew, William and Long, Yanan and Wang, Zijie J. and Talat, Zeerak and Ghosh, Avijit and Dennler, Nathaniel and Noseworthy, Michael and Jha, Sharvani and Baylor, Emi and Joshi, Aditya and Bilenko, Natalia Y. and Mcnamara, Andrew and Gontijo-Lopes, Raphael and Markham, Alex and Dong, Evyn and Kay, Jackie and Saraswat, Manu and Vytla, Nikhil and Stark, Luke},title={Queer In AI: A Case Study in Community-Led Participatory AI},year={2023},url={https://doi.org/10.1145/3593013.3594134},booktitle={Conference on Fairness, Accountability, and Transparency},month=jan}
λ-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces
Claas A. Voelcker, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, and Amir-massoud Farahmand
@article{voelcker2023lambda,title={$\lambda$-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces},author={Voelcker, Claas A. and Ahmadian, Arash and Abachi, Romina and Gilitschenski, Igor and Farahmand, Amir-massoud},journal={arXiv preprint arXiv:2306.17366},year={2023},month=nov}
2022
VIPer: Iterative Value-Aware Model Learning on the Value Improvement Path
Romina Abachi, Claas A. Voelcker, Animesh Garg, and Amir-massoud Farahmand
In Decision Awareness in Reinforcement Learning Workshop at ICML, Jul 2022
@inproceedings{abachi2022viper,title={{VIP}er: Iterative Value-Aware Model Learning on the Value Improvement Path},author={Abachi, Romina and Voelcker, Claas A. and Garg, Animesh and Farahmand, Amir-massoud},booktitle={Decision Awareness in Reinforcement Learning Workshop at ICML},year={2022},url={https://openreview.net/forum?id=qLuxVmnB7Gg},month=jul}
Value Gradient weighted Model-Based Reinforcement Learning
Claas A. Voelcker, Victor Liao, Animesh Garg, and Amir-massoud Farahmand
In International Conference on Learning Representations, Apr 2022
@inproceedings{voelcker2022value,title={Value Gradient weighted Model-Based Reinforcement Learning},author={Voelcker, Claas A. and Liao, Victor and Garg, Animesh and Farahmand, Amir-massoud},booktitle={International Conference on Learning Representations},year={2022},url={https://openreview.net/forum?id=4-D6CZkRXxI},month=apr}
2020
Structured Object-Aware Physics Prediction for Video Modeling and Planning
Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas A. Voelcker, and Kristian Kersting
In International Conference on Learning Representations, Apr 2020
@inproceedings{Kossen2020Structured,title={Structured Object-Aware Physics Prediction for Video Modeling and Planning},author={Kossen, Jannik and Stelzner, Karl and Hussing, Marcel and Voelcker, Claas A. and Kersting, Kristian},booktitle={International Conference on Learning Representations},year={2020},url={https://openreview.net/forum?id=B1e-kxSKDH},month=apr}
2019
DeepNotebooks: deep probabilistic models construct python notebooks for reporting datasets
Claas A. Voelcker, Alejandro Molina, Johannes Neumann, Dirk Westermann, and Kristian Kersting
In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I, Oct 2019
@inproceedings{volcker2020deepnotebooks,title={DeepNotebooks: deep probabilistic models construct python notebooks for reporting datasets},author={Voelcker, Claas A. and Molina, Alejandro and Neumann, Johannes and Westermann, Dirk and Kersting, Kristian},booktitle={Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, W{\"u}rzburg, Germany, September 16--20, 2019, Proceedings, Part I},year={2019},month=oct}
