Publications

Conference Publications

Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, and Somesh Jha.
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
In Proceedings of International Conference on Machine Learning (ICML), 2024
[BibTeX] [PDF]

@article{palumbo2023two,
  title={Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection},
  author={Palumbo, Nils and Guo, Yang and Wu, Xi and Chen, Jiefeng and Liang, Yingyu and Jha, Somesh},
  journal={arXiv preprint arXiv:2305.17528},
  year={2023}
}

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Somesh Jha, and Tomas Pfister.
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Transactions on Machine Learning Research, 2024
[BibTeX] [PDF]

@article{
  chen2024aspest,
  title={{ASPEST}: Bridging the Gap Between Active Learning and Selective Prediction},
  author={Jiefeng Chen and Jinsung Yoon and Sayna Ebrahimi and Sercan O Arik and Somesh Jha and Tomas Pfister},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2024},
  url={https://openreview.net/forum?id=3nprbNR3HB},
  note={}
}

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, and Somesh Jha.
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Findings of the Association for Computational Linguistics: EMNLP, 2023
[BibTeX] [PDF]

@article{chen2023adaptation,
  title={Adaptation with self-evaluation to improve selective prediction in llms},
  author={Chen, Jiefeng and Yoon, Jinsung and Ebrahimi, Sayna and Arik, Sercan O and Pfister, Tomas and Jha, Somesh},
  journal={arXiv preprint arXiv:2310.11689},
  year={2023}
}

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, and Somesh Jha.
Stratified Adversarial Robustness with Rejection
In Proceedings of International Conference on Machine Learning (ICML), 2023
[BibTeX] [PDF]

@article{chen2023stratified,
  title={Stratified Adversarial Robustness with Rejection},
  author={Chen, Jiefeng and Raghuram, Jayaram and Choi, Jihye and Wu, Xi and Liang, Yingyu and Jha, Somesh},
  journal={arXiv preprint arXiv:2305.01139},
  year={2023}
}

Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, and Atul Prakash.
Concept-based Explanations for Out-of-Distribution Detectors
In Proceedings of International Conference on Machine Learning (ICML), 2023
[BibTeX] [PDF]

@article{choi2022concept,
  title={Concept-based Explanations for Out-Of-Distribution Detectors},
  author={Choi, Jihye and Raghuram, Jayaram and Feng, Ryan and Chen, Jiefeng and Jha, Somesh and Prakash, Atul},
  journal={arXiv preprint arXiv:2203.02586},
  year={2022}
}

Jiefeng Chen, Timothy Nguyen, Dilan Gorur, and Arslan Chaudhry.
Is Forgetting Less a Good Inductive Bias for Forward Transfer?
In Proceedings of International Conference on Learning Representations (ICLR), 2023
[BibTeX] [PDF]

	@inproceedings{
	  chen2023is,
	  title={Is Forgetting Less a Good Inductive Bias for Forward Transfer?},
	  author={Jiefeng Chen and Timothy Nguyen and Dilan Gorur and Arslan Chaudhry},
	  booktitle={The Eleventh International Conference on Learning Representations },
	  year={2023},
	  url={https://openreview.net/forum?id=dL35lx-mTEs}
	}

Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, and Somesh Jha.
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
In Proceedings of International Conference on Learning Representations (ICLR), 2023
Spotlight (notable-top-25% paper)
[BibTeX] [PDF]

   @inproceedings{
     shi2023the,
     title={The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning},
     author={Zhenmei Shi and Jiefeng Chen and Kunyang Li and Jayaram Raghuram and Xi Wu and Yingyu Liang and Somesh Jha},
     booktitle={International Conference on Learning Representations},
     year={2023},
     url={https://openreview.net/forum?id=rvsbw2YthH_}
   }

Ryan Feng, Neal Mangaokar, Jiefeng Chen, Earlence Fernandes, Somesh Jha, and Atul Prakash.
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
IEEE European Symposium on Security and Privacy (EuroS&P), 2022
[BibTeX] [PDF]

 @inproceedings{feng2022graphite,
  title={GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems},
  author={Feng, Ryan and Mangaokar, Neal and Chen, Jiefeng and Fernandes, Earlence and Jha, Somesh and Prakash, Atul},
  booktitle={2022 IEEE 7th European Symposium on Security and Privacy (EuroS\&P)},
  pages={664--683},
  year={2022},
  organization={IEEE}
 }

Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, and Somesh Jha.
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
In Proceedings of International Conference on Learning Representations (ICLR), 2022
[BibTeX] [PDF]

  @article{jiefeng2021towards,
    title={Towards Evaluating the Robustness of Neural Networks Learned by Transduction},
    author={Chen, Jiefeng and Wu, Xi and Guo, Yang and Liang, Yingyu and Jha, Somesh},
    journal={arXiv preprint arXiv:2110.14735},
    year={2021}
  }

Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha.
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
Neural Information Processing Systems (NeurIPS), 2021
[BibTeX] [PDF]

   @article{chen2021detecting,
   title={Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles},
   author={Chen, Jiefeng and Liu, Frederick and Avci, Besim and Wu, Xi and Liang, Yingyu and Jha, Somesh},
   journal={arXiv preprint arXiv:2106.15728},
   year={2021}
   }

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha.
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining
In Proceedings of European Conference on Machine Learning (ECML), 2021
Acceptance Ratio: 21%
[BibTeX] [PDF]

 @article{chen2020atom,
 title={ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining},
 author={Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh},
 journal={arXiv preprint arXiv:2006.15207},
 year={2020}
 }

Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, and Xi Wu.
Concise Explanations of Neural Networks using Adversarial Training
In Proceedings of International Conference on Machine Learning (ICML), 2020
[BibTeX] [PDF]

@article{chalasani2018concise,
  title={Concise explanations of neural networks using adversarial training},
  author={Chalasani, Prasad and Chen, Jiefeng and Chowdhury, Amrita Roy and Jha, Somesh and Wu, Xi},
  journal={arXiv},
  pages={arXiv--1810},
  year={2018}
}

Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, and Somesh Jha.
Robust Attribution Regularization
Neural Information Processing Systems (NeurIPS), 2019
[BibTeX] [PDF]

@inproceedings{chen2019robust,
  title={Robust attribution regularization},
  author={Chen, Jiefeng and Wu, Xi and Rastogi, Vaibhav and Liang, Yingyu and Jha, Somesh},
  booktitle={Advances in Neural Information Processing Systems},
  pages={14300--14310},
  year={2019}
}

Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, and Somesh Jha.
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks
IEEE European Symposium on Security and Privacy (EuroS&P), 2019
[BibTeX] [PDF]

@inproceedings{chen2019towards,
  title={Towards understanding limitations of pixel discretization against adversarial attacks},
  author={Chen, Jiefeng and Wu, Xi and Rastogi, Vaibhav and Liang, Yingyu and Jha, Somesh},
  booktitle={2019 IEEE European Symposium on Security and Privacy (EuroS\&P)},
  pages={480--495},
  year={2019},
  organization={IEEE}
}

Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, and Somesh Jha.
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
In Proceedings of International Conference on Machine Learning (ICML), 2018
[BibTeX] [PDF]

@inproceedings{wu2018reinforcing,
  title={Reinforcing adversarial robustness using model confidence induced by adversarial training},
  author={Wu, Xi and Jang, Uyeong and Chen, Jiefeng and Chen, Lingjiao and Jha, Somesh},
  booktitle={International Conference on Machine Learning},
  pages={5334--5342},
  year={2018}
}

Journal Publications

Yu Shi, Jiefeng Chen, and Qi Li.
The Effects of Self-Driving Vehicles on Traffic Capacity
The Journal of Undergraduate Mathematics and its Applications (The UMAP Journal), 2017
[BibTeX] [PDF]

@article{shi2017effects,
  title={The Effects of Self-Driving Vehicles on Traffic Capacity.},
  author={Shi, Yu and Chen, Jiefeng and Li, Qi},
  journal={UMAP Journal},
  volume={38},
  number={3},
  year={2017}
}

Workshop Publications

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Somesh Jha, and Tomas Pfister.
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
ICLR Workshop on What do we need for successful domain generalization, 2023
Spotlight Presentation
[BibTeX] [PDF]

   @inproceedings{chen2023aspest,
   title={ASPEST: Bridging the Gap Between Active Learning and Selective Prediction},
   author={Chen, Jiefeng and Yoon, Jinsung and Ebrahimi, Sayna and Arik, Sercan and Jha, Somesh and Pfister, Tomas},
   booktitle={ICLR Workshop on What do we need for successful domain generalization},
   year={2023}
   }

Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, and Somesh Jha.
Best of Both Worlds: Towards Adversarial Robustness with Transduction and Rejection
NeurIPS ML Safety Workshop, 2022
[BibTeX] [PDF]

  @inproceedings{palumbobest,
  title={Best of Both Worlds: Towards Adversarial Robustness with Transduction and Rejection},
  author={Palumbo, Nils and Wu, Xi and Guo, Yang and Chen, Jiefeng and Liang, Yingyu and Jha, Somesh},
  booktitle={NeurIPS ML Safety Workshop},
  year={2022}
  }

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, and Somesh Jha.
Revisiting Adversarial Robustness of Classifiers With a Reject Option
AAAI Workshop on Adversarial Machine Learning and Beyond, 2022
Oral Presentation and Best Paper Award
[BibTeX] [PDF]

 @inproceedings{chen2021revisiting,
  title={Revisiting Adversarial Robustness of Classifiers With a Reject Option},
  author={Chen, Jiefeng and Raghuram, Jayaram and Choi, Jihye and Wu, Xi and Liang, Yingyu and Jha, Somesh},
  booktitle={The AAAI-22 Workshop on Adversarial Machine Learning and Beyond},
  year={2021}
 }

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha.
Robust Out-of-distribution Detection for Neural Networks
AAAI Workshop on Adversarial Machine Learning and Beyond, 2022
[BibTeX] [PDF]

 @article{chen2020robust,
  title={Robust out-of-distribution detection for neural networks},
  author={Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh},
  journal={arXiv preprint arXiv:2003.09711},
  year={2020}
 }

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha.
Robust Out-of-distribution Detection via Informative Outlier Mining
ICML Workshop on Uncertainty & Robustness in Deep Learning (ICML UDL), 2020
[BibTeX] [PDF]

@article{chen2020robust-new,
title={Robust Out-of-distribution Detection via Informative Outlier Mining},
author={Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh},
journal={arXiv preprint arXiv:2006.15207},
year={2020}
}

Preprints

Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, and Prasad Chalasani.
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
arXiv preprint, 2020
[BibTeX] [PDF]

@article{wu2020representation,
  title={Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation},
  author={Wu, Xi and Guo, Yang and Chen, Jiefeng and Liang, Yingyu and Jha, Somesh and Chalasani, Prasad},
  journal={arXiv preprint arXiv:2004.10390},
  year={2020}
}