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Kastner, T., Rowland, M., Tang, Y., Erdogdu, M. A., & Farahmand, A.-M. (juillet 2025). Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics [Communication écrite]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. Lien externe
Kemertas, M., Farahmand, A.-M., & Jepson, A. D. (avril 2025). A truncated newton method for optimal transport [Communication écrite]. 13th International Conference on Learning Representations (ICLR 2025), Singapore, Singapore. Lien externe
Ma, A., Farahmand, A.-M., Pan, Y., Torr, P., & Gu, J. (septembre 2024). Improving Adversarial Transferability via Model Alignment [Communication écrite]. 18th European Conference on Computer Vision (ECCV 2024), Milan, Italy. Lien externe
Ma, A., Pan, Y., & Farahmand, A.-M. (février 2025). PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling [Communication écrite]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. Lien externe
Voelcker, C. A., Pedan, A., Ahmadian, A., Abachi, R., Gilitschenski, I., & Farahmand, A.-M. (février 2025). Calibrated Value-Aware Model Learning with Probabilistic Environment Models [Communication écrite]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. Lien externe
