D. N. Yaldiz*, Y. Bakman*, S. Kang, T. Zhang, B. Buyukates, S. Avestimehr, S. P. Karimireddy, “Reconsidering LLM Uncertainty Estimation Methods in the Wild”, ACL 2025.
D. N. Yaldiz*, Y. Bakman*, B. Buyukates, C. Tao, A. Ramakrishna, D. Dimitriadis, S. Avestimehr, “Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs”, NAACL Findings 2025.
E. Mushtaq, D. N. Yaldiz, Y. Bakman, J. Ding, C. Tao, D. Dimitriadis, S. Avestimehr, “CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning”, ECCV, 2024.
Y.Bakman, D. N. Yaldiz, B. Buyukates, C. Tao, D. Dimitriadis, S. Avestimehr, “MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs”, ACL, 2024.
D. N. Yaldiz*, Y.Bakman*, Y. Ezzeldin, S. Avestimehr, “Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning”, ICLR, 2024.
D. N. Yaldiz, T.Zhang, S. Avestimehr, “Secure Federated Learning against Model Poisoning Attacks via Client Filtering”, ICLR Workshop on Backdoor Attacks and Defenses in Machine Learning, 2023.
B. Khaleghi, U. Mallapa, D. N. Yaldiz, H. Yang, M. Shah, J. Kang, T. Rosing, “PatterNet: Explore and Exploit Filter Patterns for Efficient Deep Neural Networks”, Design Automation Conference (DAC), 2022.