Gradient-free Policy Architecture Search and Adaptation. - Sayna Ebrahimi; Anna Rohrbach; Trevor Darrell (CoRL, 2017)

Compositional GAN: Learning Conditional Image Composition. - Samaneh Azadi; Deepak Pathak; Sayna Ebrahimi; Trevor Darrell (CoRR, 2018)

WiCV 2019: The Sixth Women In Computer Vision Workshop. - Irene Amerini; Elena Balashova 001; Sayna Ebrahimi; Kathryn Leonard; Arsha Nagrani; Amaia Salvador (CVPR Workshops, 2019)

Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended Abstract. - Sayna Ebrahimi; Mohamed Elhoseiny; Trevor Darrell; Marcus Rohrbach (CVPR Workshops, 2019)

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders. - Edgar Schönfeld; Sayna Ebrahimi; Samarth Sinha; Trevor Darrell; Zeynep Akata (CVPR, 2019)

Generalized Zero-Shot Learning via Aligned Variational Autoencoders. - Edgar Schönfeld; Sayna Ebrahimi; Samarth Sinha; Trevor Darrell; Zeynep Akata (CVPR Workshops, 2019)

Variational Adversarial Active Learning. - Samarth Sinha; Sayna Ebrahimi; Trevor Darrell (ICCV, 2019)

Compositional GAN (Extended Abstract): Learning Image-Conditional Binary Composition. - Samaneh Azadi; Deepak Pathak; Sayna Ebrahimi; Trevor Darrell (DGS@ICLR, 2019)

Adversarial Continual Learning. - Sayna Ebrahimi; Franziska Meier; Roberto Calandra; Trevor Darrell; Marcus Rohrbach (ECCV (11), 2020)

Uncertainty-guided Continual Learning with Bayesian Neural Networks. - Sayna Ebrahimi; Mohamed Elhoseiny; Trevor Darrell; Marcus Rohrbach (ICLR, 2020)

Minimax Active Learning. - Sayna Ebrahimi; William Gan; Kamyar Salahi; Trevor Darrell (CoRR, 2020)

Compositional GAN: Learning Image-Conditional Binary Composition. - Samaneh Azadi; Deepak Pathak; Sayna Ebrahimi; Trevor Darrell (Int. J. Comput. Vis., 2020)

Mechanical Behavior of Materials at Multiscale Peridynamic Theory and Learning-based Approaches. - Sayna Ebrahimi (2020)

Predicting with Confidence on Unseen Distributions. - Devin Guillory; Vaishaal Shankar; Sayna Ebrahimi; Trevor Darrell; Ludwig Schmidt (ICCV, 2021)

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting. - Sayna Ebrahimi; Suzanne Petryk; Akash Gokul; William Gan; Joseph E. Gonzalez; Marcus Rohrbach; Trevor Darrell (ICLR, 2021)

Region-level Active Learning for Cluttered Scenes. - Michael Laielli; Giscard Biamby; Dian Chen 001; Adam Loeffler; Phat Dat Nguyen; Ross Luo; Trevor Darrell; Sayna Ebrahimi (CoRR, 2021)

On-target Adaptation. - Dequan Wang; Shaoteng Liu; Sayna Ebrahimi; Evan Shelhamer; Trevor Darrell (CoRR, 2021)

Contrastive Test-Time Adaptation. - Dian Chen 001; Dequan Wang; Trevor Darrell; Sayna Ebrahimi (CVPR, 2022)

DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning. - Zifeng Wang 002; Zizhao Zhang; Sayna Ebrahimi; Ruoxi Sun 002; Han Zhang 010; Chen-Yu Lee; Xiaoqi Ren; Guolong Su; Vincent Perot; Jennifer G. Dy; Tomas Pfister (ECCV (26), 2022)

Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image. - Shizhan Zhu; Sayna Ebrahimi; Angjoo Kanazawa; Trevor Darrell (ICLR, 2022)

Self-Supervised Pretraining Improves Self-Supervised Pretraining. - Colorado J. Reed; Xiangyu Yue 001; Ani Nrusimha; Sayna Ebrahimi; Vivek Vijaykumar; Richard Mao; Bo Li 080; Shanghang Zhang; Devin Guillory; Sean Metzger; Kurt Keutzer; Trevor Darrell (WACV, 2022)

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations. - Qingyao Sun; Kevin Murphy 002; Sayna Ebrahimi; Alexander D'Amour (CoRR, 2022)

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister; Somesh Jha (EMNLP (Findings), 2023)

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations. - Qingyao Sun; Kevin P. Murphy; Sayna Ebrahimi; Alexander D'Amour (NeurIPS, 2023)

LANISTR: Multimodal Learning from Structured and Unstructured Data. - Sayna Ebrahimi; Sercan Ö. Arik; Yihe Dong; Tomas Pfister (CoRR, 2023)

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. - Nicasia Beebe-Wang; Sayna Ebrahimi; Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

Test-Time Adaptation for Visual Document Understanding. - Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents. - James Enouen; Hootan Nakhost; Sayna Ebrahimi; Sercan Ö. Arik; Yan Liu 002; Tomas Pfister (ACL (Findings), 2024)

Mitigating Object Hallucination via Data Augmented Contrastive Tuning. - Pritam Sarkar; Sayna Ebrahimi; Ali Etemad; Ahmad Beirami; Sercan Ö. Arik; Tomas Pfister (CoRR, 2024)

CROME: Cross-Modal Adapters for Efficient Multimodal LLM. - Sayna Ebrahimi; Sercan Ö. Arik; Tejas Nama; Tomas Pfister (CoRR, 2024)

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence. - Shangbin Feng; Zifeng Wang 002; Yike Wang 002; Sayna Ebrahimi; Hamid Palangi; Lesly Miculicich; Achin Kulshrestha; Nathalie Rauschmayr; Yejin Choi 001; Yulia Tsvetkov; Chen-Yu Lee; Tomas Pfister (CoRR, 2024)

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Somesh Jha; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment. - Pritam Sarkar; Sayna Ebrahimi; Ali Etemad; Ahmad Beirami; Sercan Ö. Arik; Tomas Pfister (ICLR, 2025)

Reverse Thinking Makes LLMs Stronger Reasoners. - Justin Chih-Yao Chen; Zifeng Wang 002; Hamid Palangi; Rujun Han; Sayna Ebrahimi; Long T. Le; Vincent Perot; Swaroop Mishra; Mohit Bansal; Chen-Yu Lee; Tomas Pfister (NAACL (Long Papers), 2025)