Jasmina Gajcin

According to our database, Jasmina Gajcin authored at least 19 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models.

Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran T. Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, Elizabeth Daly

CoRR, January, 2026

"As Long as It Does What I Want, I'd Be Happy to Trust It": Exploring User Perspectives on a Scrutable Assistant for Time Management.

Jovan Jeromela, Matt Murtagh-White, Jasmina Gajcin, Alok Debnath, Dipto Barman, Owen Conlan

Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, 2026

CURE2026: Communicating Uncertainty to foster Realistic Expectations via Human-Centered Design.

Jasmina Gajcin, Jovan Jeromela, Joel Wester, Sarah Schömbs, Styliani Kleanthous, Karthikeyan Natesan Ramamurthy, Hanna Hauptmann, Rifat Mehreen Amin

Proceedings of the Companion Proceedings of the 31st International Conference on Intelligent User Interfaces, 2026

2025

Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges.

Hyo Jin Do, Zahra Ashktorab, Jasmina Gajcin, Erik Miehling, Martín Santillán Cooper, Qian Pan, Elizabeth M. Daly, Werner Geyer

CoRR, November, 2025

Who Sees the Risk? Stakeholder Conflicts and Explanatory Policies in LLM-based Risk Assessment.

Srishti Yadav, Jasmina Gajcin, Erik Miehling, Elizabeth Daly

CoRR, November, 2025

Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations.

Jasmina Gajcin, Erik Miehling, Rahul Nair, Elizabeth Daly, Radu Marinescu, Seshu Tirupathi

CoRR, October, 2025

Agentic AI Needs a Systems Theory.

Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Matthew Riemer, Djallel Bouneffouf, John T. Richards, Amit Dhurandhar, Elizabeth M. Daly, Michael Hind, Prasanna Sattigeri, Dennis Wei, Ambrish Rawat, Jasmina Gajcin, Werner Geyer

CoRR, March, 2025

Towards Personalised and User-Friendly Counterfactual Sequences for Failure Correction.

Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic

Proceedings of the Adjunct Proceedings of the 33rd ACM Conference on User Modeling, 2025

Synthetic Data for Evaluation: Supporting LLM-as-a-Judge Workflows with EvalAssist.

Martín Santillán Cooper, Zahra Ashktorab, Hyo Jin Do, Erik Miehling, Werner Geyer, Jasmina Gajcin, Elizabeth M. Daly, Qian Pan, Michael Desmond

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities.

Jasmina Gajcin, Ivana Dusparic

ACM Comput. Surv., September, 2024

ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies.

Jasmina Gajcin, Ivana Dusparic

CoRR, 2024

Semifactual Explanations for Reinforcement Learning.

Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic

Proceedings of the 12th International Conference on Human-Agent Interaction, 2024

RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning.

Jasmina Gajcin, Ivana Dusparic

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

Iterative Reward Shaping Using Human Feedback for Correcting Reward Misspecification.

Jasmina Gajcin, James McCarthy, Rahul Nair, Radu Marinescu, Elizabeth Daly, Ivana Dusparic

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Counterfactual Explanations for Reinforcement Learning Agents.

Jasmina Gajcin

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Causal Counterfactuals for Improving the Robustness of Reinforcement Learning.

Tom He, Jasmina Gajcin, Ivana Dusparic

CoRR, 2022

Counterfactual Explanations for Reinforcement Learning.

Jasmina Gajcin, Ivana Dusparic

CoRR, 2022

ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning.

Jasmina Gajcin, Ivana Dusparic

Proceedings of the Explainable and Transparent AI and Multi-Agent Systems, 2022

2021

Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents.

Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic

CoRR, 2021