comment: <> (title: “CV”)
Scene Graph to Image Generation with Contextualized Object Layout Refinement
@inproceedings{ivgi2021scene,
title={Scene Graph to Image Generation with Contextualized Object Layout Refinement},
author={Ivgi, Maor and Benny, Yaniv and Ben-David, Avichai and Berant, Jonathan and Wolf, Lior},
booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
pages={2428--2432},
year={2021},
organization={IEEE} }
)
Achieving Model Robustness through Discrete Adversarial Training
@inproceedings{Ivgi2021AchievingMR,
title={Achieving Model Robustness through Discrete Adversarial Training},
author={Maor Ivgi and Jonathan Berant},
booktitle={EMNLP},
year={2021} }
)
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
@Article{info13010015,
AUTHOR = {Ghorbani, Amirata and Berenbaum, Dina and Ivgi, Maor and Dafna, Yuval and Zou, James Y.},
TITLE = {Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {1},
ARTICLE-NUMBER = {15},
URL = {https://www.mdpi.com/2078-2489/13/1/15},
ISSN = {2078-2489},
ABSTRACT = {Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data are one of the most commonly used modes of data in diverse applications such as healthcare and finance. Much of the existing interpretability methods used for tabular data only report feature-importance scores—either locally (per example) or globally (per model)—but they do not provide interpretation or visualization of how the features interact. We address this limitation by introducing Feature Vectors, a new global interpretability method designed for tabular datasets. In addition to providing feature-importance, Feature Vectors discovers the inherent semantic relationship among features via an intuitive feature visualization technique. Our systematic experiments demonstrate the empirical utility of this new method by applying it to several real-world datasets. We further provide an easy-to-use Python package for Feature Vectors.},
DOI = {10.3390/info13010015}
}
)
SCROLLS: Standardized CompaRison Over Long Language Sequences
@misc{shaham2022scrolls,
title={SCROLLS: Standardized CompaRison Over Long Language Sequences},
author={Uri Shaham and Elad Segal and Maor Ivgi and Avia Efrat and Ori Yoran and Adi Haviv and Ankit Gupta and Wenhan Xiong and Mor Geva and Jonathan Berant and Omer Levy},
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.823",
}
)
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments
@misc{ivgi2022scaling,
title={Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments},
author={Maor Ivgi and Yair Carmon and Jonathan Berant},
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.544",
}
)
Efficient Long-Text Understanding with Short-Text Models
@inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
)
DoG is SGD’s Best Friend: A Parameter-Free Dynamic Step Size Schedule
@article{ivgi2023dog,
title={{D}o{G} is {SGD}'s Best Friend: A Parameter-Free Dynamic Step Size Schedule},
author={Maor Ivgi and Oliver Hinder and Yair Carmon},
journal={arXiv:2302.12022},
year={2023},
}
)
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
@misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
)
Accelerated Parameter-Free Stochastic Optimization
@article{kreisler2024accelerated,
title={Accelerated Parameter-Free Stochastic Optimization},
author={Itai Kreisler and Maor Ivgi and Oliver Hinder and Yair Carmon},
journal={arXiv:2404.00666},
year={2024},
}
)
)
)
)