A Robust Bayesian Truth Serum for Non-Binary Signals. - Goran Radanovic; Boi Faltings (AAAI, 2013)

Incentives for Truthful Information Elicitation of Continuous Signals. - Goran Radanovic; Boi Faltings (AAAI, 2014)

Incentives for Subjective Evaluations with Private Beliefs. - Goran Radanovic; Boi Faltings (AAAI, 2015)

Incentive Schemes for Participatory Sensing. - Goran Radanovic; Boi Faltings (AAMAS, 2015)

Incentivizing truthful responses with the logarithmic peer truth serum. - Goran Radanovic; Boi Faltings (UbiComp/ISWC Adjunct, 2015)

Limiting the Influence of Low Quality Information in Community Sensing. - Goran Radanovic; Boi Faltings (AAMAS, 2016)

Learning to Scale Payments in Crowdsourcing with PropeRBoost. - Goran Radanovic; Boi Faltings (HCOMP, 2016)

Incentives for Effort in Crowdsourcing Using the Peer Truth Serum. - Goran Radanovic; Boi Faltings; Radu Jurca (ACM Trans. Intell. Syst. Technol., 2016)

Elicitation and Aggregation of Crowd Information. - Goran Radanovic (2016)

Multi-View Decision Processes: The Helper-AI Problem. - Christos Dimitrakakis; David C. Parkes; Goran Radanovic; Paul Tylkin (NIPS, 2017)

Subjective fairness: Fairness is in the eye of the beholder. - Christos Dimitrakakis; Yang Liu 018; David C. Parkes; Goran Radanovic (CoRR, 2017)

Peer Truth Serum: Incentives for Crowdsourcing Measurements and Opinions. - Boi Faltings; Radu Jurca; Goran Radanovic (CoRR, 2017)

Calibrated Fairness in Bandits. - Yang Liu 018; Goran Radanovic; Christos Dimitrakakis; Debmalya Mandal; David C. Parkes (CoRR, 2017)

Mechanismen zur Beschaffung korrekter Daten. - Boi Faltings; Goran Radanovic (Inform. Spektrum, 2017)

Game Theory for Data Science: Eliciting Truthful Information - Boi Faltings; Goran Radanovic (Game Theory for Data Science, 2017)

Partial Truthfulness in Minimal Peer Prediction Mechanisms With Limited Knowledge. - Goran Radanovic; Boi Faltings (AAAI, 2018)

Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints. - Goran Radanovic; Adish Singla; Andreas Krause 001; Boi Faltings (AAAI, 2018)

Bayesian Fairness. - Christos Dimitrakakis; Yang Liu 018; David C. Parkes; Goran Radanovic (AAAI, 2019)

How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness. - Nripsuta Ani Saxena; Karen Huang; Evan DeFilippis; Goran Radanovic; David C. Parkes; Yang Liu 018 (AIES, 2019)

Learning to Collaborate in Markov Decision Processes. - Goran Radanovic; Rati Devidze; David C. Parkes; Adish Singla (ICML, 2019)

The Effectiveness of Peer Prediction in Long-Term Forecasting. - Debmalya Mandal; Goran Radanovic; David C. Parkes (AAAI, 2020)

Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. - Amin Rakhsha; Goran Radanovic; Rati Devidze; Xiaojin Zhu 001; Adish Singla (ICML, 2020)

How do fairness definitions fare? Testing public attitudes towards three algorithmic definitions of fairness in loan allocations. - Nripsuta Ani Saxena; Karen Huang; Evan DeFilippis; Goran Radanovic; David C. Parkes; Yang Liu 018 (Artif. Intell., 2020)

Diversity in News Recommendations. - Abraham Bernstein; Claes H. de Vreese; Natali Helberger; Wolfgang Schulz 002; Katharina Anna Zweig; Christian Baden; Michael A. Beam; Marc P. Hauer; Lucien Heitz; Pascal Jürgens; Christian Katzenbach; Benjamin Kille; et al. (CoRR, 2020)

Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments. - Paul Tylkin; Goran Radanovic; David C. Parkes (AAMAS, 2021)

Explicable Reward Design for Reinforcement Learning Agents. - Rati Devidze; Goran Radanovic; Parameswaran Kamalaruban; Adish Singla (NeurIPS, 2021)

On Blame Attribution for Accountable Multi-Agent Sequential Decision Making. - Stelios Triantafyllou; Adish Singla; Goran Radanovic (NeurIPS, 2021)

Reinforcement Learning for Education: Opportunities and Challenges. - Adish Singla; Anna N. Rafferty; Goran Radanovic; Neil T. Heffernan (CoRR, 2021)

Diversity in News Recommendation (Dagstuhl Perspectives Workshop 19482). - Abraham Bernstein; Claes H. de Vreese; Natali Helberger; Wolfgang Schulz 002; Katharina Anna Zweig; Christian Baden; Michael A. Beam; Marc P. Hauer; Lucien Heitz; Pascal Jürgens; Christian Katzenbach; Benjamin Kille; et al. (Dagstuhl Manifestos, 2021)

Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. - Amin Rakhsha; Goran Radanovic; Rati Devidze; Xiaojin Zhu 001; Adish Singla (J. Mach. Learn. Res., 2021)

Admissible Policy Teaching through Reward Design. - Kiarash Banihashem; Adish Singla; Jiarui Gan; Goran Radanovic (AAAI, 2022)

Bayesian Persuasion in Sequential Decision-Making. - Jiarui Gan; Rupak Majumdar; Goran Radanovic; Adish Singla (AAAI, 2022)

Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes. - Stelios Triantafyllou; Adish Singla; Goran Radanovic (AIES, 2022)

Sequential Decision Making With Information Asymmetry (Invited Talk). - Jiarui Gan; Rupak Majumdar; Goran Radanovic; Adish Singla (CONCUR, 2022)

Envy-free Policy Teaching to Multiple Agents. - Jiarui Gan; Rupak Majumdar; Adish Singla; Goran Radanovic (NeurIPS, 2022)

Markov Decision Processes with Time-Varying Geometric Discounting. - Jiarui Gan; Annika Hennes; Rupak Majumdar; Debmalya Mandal; Goran Radanovic (AAAI, 2023)

Online Reinforcement Learning with Uncertain Episode Lengths. - Debmalya Mandal; Goran Radanovic; Jiarui Gan; Adish Singla; Rupak Majumdar (AAAI, 2023)

Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning. - Andi Nika; Adish Singla; Goran Radanovic (AISTATS, 2023)

Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks. - Mohammad Mohammadi; Jonathan Nöther; Debmalya Mandal; Adish Singla; Goran Radanovic (AAMAS, 2023)

Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs. - Stelios Triantafyllou; Goran Radanovic (AAMAS, 2023)

Performative Reinforcement Learning. - Debmalya Mandal; Stelios Triantafyllou; Goran Radanovic (ICML, 2023)

Sequential Principal-Agent Problems with Communication: Efficient Computation and Learning. - Jiarui Gan; Rupak Majumdar; Debmalya Mandal; Goran Radanovic (CoRR, 2023)

Agent-Specific Effects. - Stelios Triantafyllou; Aleksa Sukovic; Debmalya Mandal; Goran Radanovic (CoRR, 2023)

Defense Against Reward Poisoning Attacks in Reinforcement Learning. - Kiarash Banihashem; Adish Singla; Goran Radanovic (Trans. Mach. Learn. Res., 2023)

Corruption-Robust Offline Two-Player Zero-Sum Markov Games. - Andi Nika; Debmalya Mandal; Adish Singla; Goran Radanovic (AISTATS, 2024)

Reward Design for Justifiable Sequential Decision-Making. - Aleksa Sukovic; Goran Radanovic (ICLR, 2024)

Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences. - Andi Nika; Debmalya Mandal; Parameswaran Kamalaruban; Georgios Tzannetos; Goran Radanovic; Adish Singla (ICML, 2024)

Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs. - Stelios Triantafyllou; Aleksa Sukovic; Debmalya Mandal; Goran Radanovic (ICML, 2024)

Learning Embeddings for Sequential Tasks Using Population of Agents. - Mridul Mahajan; Georgios Tzannetos; Goran Radanovic; Adish Singla (IJCAI, 2024)

Performative Reinforcement Learning in Gradually Shifting Environments. - Ben Rank; Stelios Triantafyllou; Debmalya Mandal; Goran Radanovic (UAI, 2024)

Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making. - Stelios Triantafyllou; Aleksa Sukovic; Yasaman Zolfimoselo; Goran Radanovic (CoRR, 2024)

Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria. - Andi Nika; Jonathan Nöther; Adish Singla; Goran Radanovic (Trans. Mach. Learn. Res., 2024)

Text-Diffusion Red-Teaming of Large Language Models: Unveiling Harmful Behaviors with Proximity Constraints. - Jonathan Nöther; Adish Singla; Goran Radanovic (AAAI, 2025)

On Corruption-Robustness in Performative Reinforcement Learning. - Vasilis Pollatos; Debmalya Mandal; Goran Radanovic (AAAI, 2025)

Corruption Robust Offline Reinforcement Learning with Human Feedback. - Debmalya Mandal; Andi Nika; Parameswaran Kamalaruban; Adish Singla; Goran Radanovic (AISTATS, 2025)

Performative Reinforcement Learning with Linear Markov Decision Process. - Debmalya Mandal; Goran Radanovic (AISTATS, 2025)

Policy Teaching via Data Poisoning in Learning from Human Preferences. - Andi Nika; Jonathan Nöther; Debmalya Mandal; Parameswaran Kamalaruban; Adish Singla; Goran Radanovic (AISTATS, 2025)

Independent Learning in Performative Markov Potential Games. - Rilind Sahitaj; Paulius Sasnauskas; Yigit Yalin; Debmalya Mandal; Goran Radanovic (AISTATS, 2025)

Distributionally Robust Reinforcement Learning with Human Feedback. - Debmalya Mandal; Paulius Sasnauskas; Goran Radanovic (CoRR, 2025)

Can In-Context Reinforcement Learning Recover From Reward Poisoning Attacks? - Paulius Sasnauskas; Yigit Yalin; Goran Radanovic (CoRR, 2025)