2025

• Stelios Triantafyllou, Aleksa Sukovic, Yasaman Zolfi Moselo, Goran Radanovic. 2025. Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making. To Appear: In Proceedings of the 42nd International Conference on Machine Learning (ICML'25).
• Jonathan Nöther, Adish Singla, Goran Radanovic. 2025. Text-Diffusion Red-Teaming of Large Language Models: Unveiling Harmful Behaviors with Proximity Constraints. In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25).
• Vasilis Pollatos, Debmalya Mandal, Goran Radanovic. 2025. On Corruption-Robustness in Performative Reinforcement Learning. In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25).
• Andi Nika, Jonathan Nöther, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic. 2025. Policy Teaching via Data Poisoning in Learning from Human Preferences. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS'25).
• Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın, Debmalya Mandal, Goran Radanovic. 2025. Independent Learning in Performative Markov Potential Games. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS'25).
• Debmalya Mandal, Parameswaran Kamalaruban, Andi Nika, Adish Singla, Goran Radanovic. 2025. Corruption Robust Offline Reinforcement Learning with Human Feedback. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS'25).
• Debmalya Mandal, Goran Radanovic. 2025. Performative Reinforcement Learning with Linear Markov Decision Process. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS'25).

2024

• Aleksa Sukovic and Goran Radanovic. 2024. Reward Design for Justifiable Sequential Decision-Making. In International Conference on Learning Representations (ICLR'24).
• Stelios Triantafyllou, Aleksa Sukovic, Debmalya Mandal, Goran Radanovic. 2024. Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs. In Proceedings of the 41st International Conference on Machine Learning (ICML'24).
• Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Georgios Tzannetos, Goran Radanovic, Adish Singla. 2024. Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences. In Proceedings of the 41st International Conference on Machine Learning (ICML'24).
• Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic. 2024. Corruption-robust Offline Two-player Zero-sum Markov Games. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS'24).
• Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic. 2024. Performative Reinforcement Learning in Gradually Shifting Environments. In Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI'24).
• Mridul Mahajan, Georgios Tzannetos, Goran Radanovic, Adish Singla. 2024. Learning Embeddings for Sequential Tasks Using Population of Agents. In Proceedings of the the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24).
• Andi Nika, Jonathan Nöther, Adish Singla, Goran Radanovic. 2024. Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria. Transactions on Machine Learning Research (TMLR).
• Jiarui Gan, Rupak Majumdar, Debmalya Mandal, Goran Radanovic. 2024. Sequential Principal-Agent Problems with Communication: Efficient Computation and Learning. arXiv:2306.03832.

2023

• Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic. 2023. Performative Reinforcement Learning. In Proceedings of the 40th International Conference on Machine Learning (ICML'23).
• Stelios Triantafyllou, Goran Radanovic. 2023. Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs. In Proceedings of the 22nd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'23).
• Mohammad Mohammadi, Jonathan Nöther, Debmalya Mandal, Adish Singla, Goran Radanovic. 2023. Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks. In Proceedings of the 22nd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'23).
• Andi Nika, Adish Singla, Goran Radanovic. 2023. Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS'23).
• Kiarash Banihashem, Adish Singla, Goran Radanovic. 2023. Defense Against Reward Poisoning Attacks in Reinforcement Learning. Transactions on Machine Learning Research (TMLR).
• Debmalya Mandal, Goran Radanovic, Jiarui Gan, Adish Singla, Rupak Majumdar. 2023. Online Reinforcement Learning with Uncertain Episode Lengths. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23).
• Jiarui Gan, Annika Hennes, Rupak Majumdar, Debmalya Mandal, Goran Radanovic. 2023. Markov decision processes with time-varying geometric discounting. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23).

2022

• Stelios Triantafyllou, Adish Singla, Goran Radanovic. 2022. Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22).
• Kiarash Banihashem, Adish Singla, Jiarui Gan, Goran Radanovic. 2022. Admissible Policy Teaching through Reward Design. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22).
• Jiarui Gan, Rupak Majumdar, Goran Radanovic, Adish Singla. 2022. Bayesian Persuasion in Sequential Decision-Making. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22).
• Jiarui Gan, Rupak Majumdar, Adish Singla, Goran Radanovic. 2022. Envy-free Policy Teaching to Multiple Agents. In Proceedings of the 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22).

2021

• Stelios Triantafyllou, Adish Singla, Goran Radanovic. 2021. On Blame Attribution for Accountable Multi-Agent Sequential Decision Making. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21).
• Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla. 2021. Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. Journal of Machine Learning Research (JMLR), 22(210): 1-45.
• Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla. 2021. Explicable Reward Design for Reinforcement Learning Agents. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21).
• Paul Tylkin, Goran Radanovic, David C. Parkes. 2021. Learning robust helpful behaviors in two-player cooperative Atari environments. In Proceedings of the 20th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'21).
• Abraham Bernstein, Claes H. de Vreese, Natali Helberger, Wolfgang Schulz, Katharina Anna Zweig, Christian Baden, Michael A. Beam, Marc P. Hauer, Lucien Heitz, Pascal Jürgens, Christian Katzenbach, Benjamin Kille, Beate Klimkiewicz, Wiebke Loosen, Judith Möller, Goran Radanovic, Guy Shani, Nava Tintarev, Suzanne Tolmeijer, Wouter van Atteveldt, Sanne Vrijenhoek, Theresa Zueger. 2021. Diversity in News Recommendation (Dagstuhl Perspectives Workshop 19482). Dagstuhl Manifestos, 9(1): 43-61.
• Adish Singla, Anna N. Rafferty, Goran Radanovic, Neil T. Heffernan. 2021. Reinforcement Learning for Education: Opportunities and Challenges. arXiv:2107.08828.

2020

• Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla. 2020. Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. In Proceedings of the 37th International Conference on Machine Learning (ICML'20).
• Nripsuta Ani Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David C. Parkes, and Yang Liu. 2019. How do Fairness Definitions Fare? Testing Public Attitudes Towards Three Algorithmic Definitions of Fairness in Loan Allocations. Artificial Intelligence, 283:103238.
• Debmalya Mandal, Goran Radanovic, David C. Parkes. 2020. The Effectiveness of Peer Prediction in Long-Term Forecasting. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20).

2019

• Christos Dimitrakakis, Yang Liu, David C. Parkes, and Goran Radanovic. 2019. Bayesian Fairness. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19).
• Nripsuta Ani Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David C. Parkes, and Yang Liu. 2019. How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness. In Proceedings of the AAAI/ACM conference on Artificial Intelligence, Ethics, and Society (AIES'19).
• Goran Radanovic, Rati Devidze, David C. Parkes, and Adish Singla. 2019. Learning to Collaborate in Markov Decision Processes. In Proceedings of the 36th International Conference on Machine Learning (ICML'19).

2018

• Goran Radanovic, Adish Singla, Andreas Krause, and Boi Faltings. 2018. Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18).
• Goran Radanovic and Boi Faltings. 2018. Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18).

2017

• Boi Faltings and Goran Radanovic. 2017. Game Theory for Data Science: Eliciting Truthful Information. Morgan & Claypool Publishers.
• Boi Faltings and Goran Radanovic. 2017. Mechanismen zur Beschaffung korrekter Daten. Informatik-Spektrum, 40, Pp. 64–74.
• Christos Dimitrakakis, David C. Parkes, Goran Radanovic, and Paul Tylkin. 2017. Multi-View Decision Processes: The Helper-AI Problem. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NeurIPS'17).
• Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, David C. Parkes. 2017. Calibrated Fairness in Bandits. The 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017).
• Boi Faltings, Radu Jurca, and Goran Radanovic. 2017. Peer Truth Serum: Incentives for Crowdsourcing Measurements and Opinions. arXiv:1704.05269.

2016

• Goran Radanovic. 2016. Elicitation and Aggregation of Crowd Information. PhD Thesis.
• Goran Radanovic, Boi Faltings, and Radu Jurca. 2016. Incentives for effort in crowdsourcing using the peer truth serum. ACM Transactions on Intelligent Systems and Technology (TIST), 7(4): 48:1–48:28.
• Goran Radanovic and Boi Faltings. 2016. Learning to Scale Payments in Crowdsourcing with PropeRBoost. In Proceedings of the Fourth AAAI conference on Conference on Human Computation and Crowdsourcing (HCOMP'16).
• Goran Radanovic and Boi Faltings. 2016. Limiting the Influence of Low Quality Information in Community Sensing. In Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (AAMAS'16).

2015

• Goran Radanovic and Boi Faltings. 2015. Incentive Schemes for Participatory Sensing. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (AAMAS'15).
• Goran Radanovic and Boi Faltings. 2015. Incentivizing truthful responses with the logarithmic peer truth serum. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers.
• Goran Radanovic and Boi Faltings. 2015. Incentives for Subjective Evaluations with Private Beliefs. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15).

2014

• Goran Radanovic and Boi Faltings. 2014. Incentives for Truthful Information Elicitation of Continuous Signals. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14).

2013

• Goran Radanovic and Boi Faltings. 2013. A Robust Bayesian Truth Serum for Non-binary Signals. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13).