default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSoheil Feizi
Soheil Feizi-Khankandi
Person information
- affiliation: University of Maryland (UMD), Reliable AI Lab, College Park, MD, USA
- affiliation: Stanford University, Stanford, CA, USA
- affiliation (PhD): Massachusetts Institute of Technology (MIT), Research Laboratory of Electronics, Cambridge, MA, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
[c99]Samuel Audia, Soheil Feizi, Matthias Zwicker, Dinesh Manocha:
How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings. ICLR 2025- [c98]Mazda Moayeri, Sriram Balasubramanian, Samyadeep Basu, Priyatham Kattakinda, Atoosa Malemir Chegini, Robert Brauneis, Soheil Feizi:
Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models. ICLR 2025 - [c97]Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran, Safoora Yousefi, Thomas Fel, Soheil Feizi, Besmira Nushi, Neel Joshi, Vibhav Vineet:
Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models. ICLR 2025 - [i120]Samyadeep Basu, Vlad I. Morariu, Zichao Wang, Ryan A. Rossi, Cherry Zhao, Soheil Feizi, Varun Manjunatha:
On Mechanistic Circuits for Extractive Question-Answering. CoRR abs/2502.08059 (2025) - [i119]Shoumik Saha, Soheil Feizi:
Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing. CoRR abs/2502.15666 (2025) - [i118]Zihao Lin, Samyadeep Basu, Mohammad Beigi, Varun Manjunatha, Ryan A. Rossi, Zichao Wang, Yufan Zhou, Sriram Balasubramanian, Arman Zarei, Keivan Rezaei, Ying Shen, Barry Menglong Yao, Zhiyang Xu, Qin Liu, Yuxiang Zhang, Yan Sun, Shilong Liu, Li Shen, Hongxuan Li, Soheil Feizi, Lifu Huang:
A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models. CoRR abs/2502.17516 (2025) - [i117]Parsa Hosseini, Sumit Nawathe, Mazda Moayeri, Sriram Balasubramanian, Soheil Feizi:
Seeing What's Not There: Spurious Correlation in Multimodal LLMs. CoRR abs/2503.08884 (2025) - [i116]Atoosa Malemir Chegini, Keivan Rezaei, Hamid Eghbalzadeh, Soheil Feizi:
RePanda: Pandas-powered Tabular Verification and Reasoning. CoRR abs/2503.11921 (2025) - [i115]Samuel Audia, Soheil Feizi, Matthias Zwicker, Dinesh Manocha:
How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings. CoRR abs/2504.13412 (2025) - 2024
- [c96]Samyadeep Basu, Shell Xu Hu, Daniela Massiceti, Soheil Feizi:
Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning. AAAI 2024: 11024-11031 - [c95]Neha Mukund Kalibhat, Kanika Narang, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Measuring Self-Supervised Representation Quality for Downstream Classification Using Discriminative Features. AAAI 2024: 13031-13039 - [c94]Samyadeep Basu, Shell Xu Hu, Maziar Sanjabi, Daniela Massiceti, Soheil Feizi:
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP. EMNLP 2024: 6105-6113 - [c93]Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha:
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning. EMNLP 2024: 19584-19601 - [c92]Mazda Moayeri
, Elham Tabassi, Soheil Feizi:
WorldBench: Quantifying Geographic Disparities in LLM Factual Recall. FAccT 2024: 1211-1228 - [c91]Samyadeep Basu, Nanxuan Zhao, Vlad I. Morariu, Soheil Feizi, Varun Manjunatha:
Localizing and Editing Knowledge In Text-to-Image Generative Models. ICLR 2024 - [c90]Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri, Soheil Feizi:
PRIME: Prioritizing Interpretability in Failure Mode Extraction. ICLR 2024 - [c89]Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei, Aounon Kumar, Atoosa Malemir Chegini, Wenxiao Wang, Soheil Feizi:
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks. ICLR 2024 - [c88]Shoumik Saha, Wenxiao Wang, Yigitcan Kaya, Soheil Feizi, Tudor Dumitras:
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness. ICLR 2024 - [c87]Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Vlad I. Morariu, Nanxuan Zhao, Ryan A. Rossi, Varun Manjunatha, Soheil Feizi:
On Mechanistic Knowledge Localization in Text-to-Image Generative Models. ICML 2024 - [c86]Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Malemir Chegini, Soheil Feizi:
Fast Adversarial Attacks on Language Models In One GPU Minute. ICML 2024 - [c85]Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi:
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP. NeurIPS 2024 - [c84]Samyadeep Basu, Martin Grayson, Cecily Morrison, Besmira Nushi, Soheil Feizi, Daniela Massiceti:
Understanding Information Storage and Transfer in Multi-Modal Large Language Models. NeurIPS 2024 - [c83]Prajwal Singhania, Siddharth Singh, Shwai He, Soheil Feizi, Abhinav Bhatele:
Loki: Low-rank Keys for Efficient Sparse Attention. NeurIPS 2024 - [c82]Gaurang Sriramanan, Siddhant Bharti, Vinu Sankar Sadasivan, Shoumik Saha, Priyatham Kattakinda, Soheil Feizi:
LLM-Check: Investigating Detection of Hallucinations in Large Language Models. NeurIPS 2024 - [c81]Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri, Soheil Feizi:
Data-Centric Debugging: mitigating model failures via targeted image retrieval. WACV 2024: 63-74 - [i114]Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Malemir Chegini, Soheil Feizi:
Fast Adversarial Attacks on Language Models In One GPU Minute. CoRR abs/2402.15570 (2024) - [i113]Hamid Kazemi, Atoosa Malemir Chegini, Jonas Geiping, Soheil Feizi, Tom Goldstein:
What do we learn from inverting CLIP models? CoRR abs/2403.02580 (2024) - [i112]Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian, Priyatham Kattakinda, Atoosa Malemir Chegini, Robert Brauneis, Soheil Feizi:
Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models. CoRR abs/2404.08030 (2024) - [i111]Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Ryan A. Rossi, Cherry Zhao, Vlad I. Morariu, Varun Manjunatha, Soheil Feizi:
On Mechanistic Knowledge Localization in Text-to-Image Generative Models. CoRR abs/2405.01008 (2024) - [i110]Neha Mukund Kalibhat, Priyatham Kattakinda, Arman Zarei, Nikita Seleznev, Samuel Sharpe, Senthil Kumar, Soheil Feizi:
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning. CoRR abs/2405.16401 (2024) - [i109]Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi:
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP. CoRR abs/2406.01583 (2024) - [i108]Prajwal Singhania, Siddharth Singh, Shwai He, Soheil Feizi, Abhinav Bhatele:
Loki: Low-Rank Keys for Efficient Sparse Attention. CoRR abs/2406.02542 (2024) - [i107]Mehrdad Saberi, Vinu Sankar Sadasivan, Arman Zarei, Hessam Mahdavifar, Soheil Feizi:
DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking. CoRR abs/2406.02836 (2024) - [i106]Samyadeep Basu, Martin Grayson, Cecily Morrison, Besmira Nushi, Soheil Feizi, Daniela Massiceti:
Understanding Information Storage and Transfer in Multi-modal Large Language Models. CoRR abs/2406.04236 (2024) - [i105]Arman Zarei, Keivan Rezaei, Samyadeep Basu, Mehrdad Saberi, Mazda Moayeri, Priyatham Kattakinda, Soheil Feizi:
Understanding and Mitigating Compositional Issues in Text-to-Image Generative Models. CoRR abs/2406.07844 (2024) - [i104]Donghyeon Joo, Ramyad Hadidi, Soheil Feizi, Bahar Asgari:
Endor: Hardware-Friendly Sparse Format for Offloaded LLM Inference. CoRR abs/2406.11674 (2024) - [i103]Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha:
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning. CoRR abs/2406.13683 (2024) - [i102]Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Gong, Mia Hoffmann, Somesh Jha, Zhengyuan Jiang, Mehrdad Saberi Kamarposhti, John C. Mitchell, Jessica Newman, Emelia Probasco, Yanjun Qi, Khawaja Shams, Matthew Turek:
Securing the Future of GenAI: Policy and Technology. CoRR abs/2407.12999 (2024) - [i101]Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran, Safoora Yousefi, Thomas Fel, Soheil Feizi, Besmira Nushi, Neel Joshi, Vibhav Vineet:
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models. CoRR abs/2410.13826 (2024) - [i100]Keivan Rezaei, Khyathi Raghavi Chandu, Soheil Feizi, Yejin Choi, Faeze Brahman, Abhilasha Ravichander:
RESTOR: Knowledge Recovery through Machine Unlearning. CoRR abs/2411.00204 (2024) - [i99]Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Zhenqiang Gong, Mia Hoffmann, Somesh Jha, Zhengyuan Jiang, Mehrdad Saberi Kamarposhti, John C. Mitchell, Jessica Newman, Emelia Probasco, Yanjun Qi, Khawaja Shams, Matthew Turek:
Securing the Future of GenAI: Policy and Technology. IACR Cryptol. ePrint Arch. 2024: 855 (2024) - 2023
- [j14]Clark W. Barrett, Brad Boyd, Elie Bursztein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. Found. Trends Priv. Secur. 6(1): 1-52 (2023) - [j13]Chun Pong Lau, Jiang Liu, Hossein Souri, Wei-An Lin, Soheil Feizi, Rama Chellappa:
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 13054-13067 (2023) - [j12]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts. Trans. Mach. Learn. Res. 2023 (2023) - [c80]Alexander Levine, Soheil Feizi:
Goal-Conditioned Q-learning as Knowledge Distillation. AAAI 2023: 8500-8509 - [c79]Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi:
Adapting Self-Supervised Representations to Multi-Domain Setups. BMVC 2023: 353-355 - [c78]Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi:
Text2Concept: Concept Activation Vectors Directly from Text. CVPR Workshops 2023: 3744-3749 - [c77]Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi:
CUDA: Convolution-Based Unlearnable Datasets. CVPR 2023: 3862-3871 - [c76]Sriram Balasubramanian, Soheil Feizi:
Towards Improved Input Masking for Convolutional Neural Networks. ICCV 2023: 1855-1865 - [c75]Samyadeep Basu, Megan Stanley, John Bronskill, Soheil Feizi, Daniela Massiceti:
Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks. ICLR 2023 - [c74]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. ICLR 2023 - [c73]Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang:
Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication. ICLR 2023 - [c72]Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. ICML 2023: 15623-15638 - [c71]Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi:
Text-To-Concept (and Back) via Cross-Model Alignment. ICML 2023: 25037-25060 - [c70]Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, Soheil Feizi:
Run-off Election: Improved Provable Defense against Data Poisoning Attacks. ICML 2023: 29030-29050 - [c69]Wenxiao Wang, Soheil Feizi:
Temporal Robustness against Data poisoning. NeurIPS 2023 - [c68]Sriram Balasubramanian, Gaurang Sriramanan, Vinu Sankar Sadasivan, Soheil Feizi:
Exploring Geometry of Blind Spots in Vision models. NeurIPS 2023 - [c67]Mazda Moayeri, Wenxiao Wang, Sahil Singla, Soheil Feizi:
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases. NeurIPS 2023 - [c66]Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Soheil Feizi, Adrian Weller:
Diffused Redundancy in Pre-trained Representations. NeurIPS 2023 - [c65]Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi:
Text2Concept: Concept Activation Vectors Directly From Text. XAI4CV 2023: 3744-3749 - [i98]Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, Soheil Feizi:
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks. CoRR abs/2302.02300 (2023) - [i97]Wenxiao Wang, Soheil Feizi:
Temporal Robustness against Data Poisoning. CoRR abs/2302.03684 (2023) - [i96]Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi:
CUDA: Convolution-based Unlearnable Datasets. CoRR abs/2303.04278 (2023) - [i95]Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi:
Can AI-Generated Text be Reliably Detected? CoRR abs/2303.11156 (2023) - [i94]Shoumik Saha, Wenxiao Wang, Yigitcan Kaya, Soheil Feizi:
Adversarial Robustness of Learning-based Static Malware Classifiers. CoRR abs/2303.13372 (2023) - [i93]Aounon Kumar, Vinu Sankar Sadasivan, Soheil Feizi:
Provable Robustness for Streaming Models with a Sliding Window. CoRR abs/2303.16308 (2023) - [i92]Samyadeep Basu, Daniela Massiceti, Shell Xu Hu, Soheil Feizi:
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning. CoRR abs/2304.01917 (2023) - [i91]Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi:
Text-To-Concept (and Back) via Cross-Model Alignment. CoRR abs/2305.06386 (2023) - [i90]Vedant Nanda, Till Speicher, John P. Dickerson, Soheil Feizi, Krishna P. Gummadi, Adrian Weller:
Diffused Redundancy in Pre-trained Representations. CoRR abs/2306.00183 (2023) - [i89]Wenxiao Wang, Soheil Feizi:
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks. CoRR abs/2306.16415 (2023) - [i88]Samyadeep Basu, Maziar Sanjabi, Daniela Massiceti, Shell Xu Hu, Soheil Feizi:
Augmenting CLIP with Improved Visio-Linguistic Reasoning. CoRR abs/2307.09233 (2023) - [i87]Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. CoRR abs/2307.10504 (2023) - [i86]Clark W. Barrett, Brad Boyd, Ellie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. CoRR abs/2308.14840 (2023) - [i85]Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju:
Certifying LLM Safety against Adversarial Prompting. CoRR abs/2309.02705 (2023) - [i84]Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi:
Adapting Self-Supervised Representations to Multi-Domain Setups. CoRR abs/2309.03999 (2023) - [i83]Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei, Aounon Kumar, Atoosa Malemir Chegini, Wenxiao Wang, Soheil Feizi:
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks. CoRR abs/2310.00076 (2023) - [i82]Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri, Soheil Feizi:
PRIME: Prioritizing Interpretability in Failure Mode Extraction. CoRR abs/2310.00164 (2023) - [i81]Samyadeep Basu, Mehrdad Saberi, Shweta Bhardwaj, Atoosa Malemir Chegini, Daniela Massiceti, Maziar Sanjabi, Shell Xu Hu, Soheil Feizi:
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods. CoRR abs/2310.02426 (2023) - [i80]Samyadeep Basu, Nanxuan Zhao, Vlad I. Morariu, Soheil Feizi, Varun Manjunatha:
Localizing and Editing Knowledge in Text-to-Image Generative Models. CoRR abs/2310.13730 (2023) - [i79]Sriram Balasubramanian, Gaurang Sriramanan, Vinu Sankar Sadasivan, Soheil Feizi:
Exploring Geometry of Blind Spots in Vision Models. CoRR abs/2310.19889 (2023) - [i78]Soheil Feizi, MohammadTaghi Hajiaghayi, Keivan Rezaei, Suho Shin:
Online Advertisements with LLMs: Opportunities and Challenges. CoRR abs/2311.07601 (2023) - [i77]Jiang Liu, Chen Wei, Yuxiang Guo, Heng Yu, Alan L. Yuille, Soheil Feizi, Chun Pong Lau, Rama Chellappa:
Instruct2Attack: Language-Guided Semantic Adversarial Attacks. CoRR abs/2311.15551 (2023) - [i76]Atoosa Malemir Chegini, Soheil Feizi:
Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generation. CoRR abs/2312.05464 (2023) - 2022
- [j11]Jiang Liu, Chun Pong Lau, Hossein Souri, Soheil Feizi, Rama Chellappa:
Mutual Adversarial Training: Learning Together is Better Than Going Alone. IEEE Trans. Inf. Forensics Secur. 17: 2364-2377 (2022) - [c64]Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. AISTATS 2022: 9908-9942 - [c63]Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi:
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection. CVPR 2022: 14953-14962 - [c62]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CVPR 2022: 19065-19075 - [c61]Sahil Singla, Soheil Feizi:
Salient ImageNet: How to discover spurious features in Deep Learning? ICLR 2022 - [c60]Sahil Singla, Surbhi Singla, Soheil Feizi:
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100. ICLR 2022 - [c59]Aounon Kumar, Alexander Levine, Soheil Feizi:
Policy Smoothing for Provably Robust Reinforcement Learning. ICLR 2022 - [c58]Priyatham Kattakinda, Soheil Feizi:
FOCUS: Familiar Objects in Common and Uncommon Settings. ICML 2022: 10825-10847 - [c57]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. ICML 2022: 22769-22783 - [c56]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Lethal Dose Conjecture on Data Poisoning. NeurIPS 2022 - [c55]Sahil Singla, Soheil Feizi:
Improved techniques for deterministic l2 robustness. NeurIPS 2022 - [c54]Mazda Moayeri, Sahil Singla, Soheil Feizi:
Hard ImageNet: Segmentations for Objects with Strong Spurious Cues. NeurIPS 2022 - [c53]Mazda Moayeri, Kiarash Banihashem, Soheil Feizi:
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness. NeurIPS 2022 - [c52]Gaurang Sriramanan, Maharshi Gor, Soheil Feizi:
Toward Efficient Robust Training against Union of $\ell_p$ Threat Models. NeurIPS 2022 - [i75]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CoRR abs/2201.10766 (2022) - [i74]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Certifying Model Accuracy under Distribution Shifts. CoRR abs/2201.12440 (2022) - [i73]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. CoRR abs/2202.02628 (2022) - [i72]Neha Mukund Kalibhat, Kanika Narang, Liang Tan, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Understanding Failure Modes of Self-Supervised Learning. CoRR abs/2203.01881 (2022) - [i71]Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. CoRR abs/2203.08945 (2022) - [i70]Sahil Singla, Mazda Moayeri, Soheil Feizi:
Core Risk Minimization using Salient ImageNet. CoRR abs/2203.15566 (2022) - [i69]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts for Structured Data. CoRR abs/2206.02107 (2022) - [i68]Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang:
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems. CoRR abs/2206.10158 (2022) - [i67]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Lethal Dose Conjecture on Data Poisoning. CoRR abs/2208.03309 (2022) - [i66]Alexander Levine, Soheil Feizi:
Goal-Conditioned Q-Learning as Knowledge Distillation. CoRR abs/2208.13298 (2022) - [i65]Mazda Moayeri, Kiarash Banihashem, Soheil Feizi:
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness. CoRR abs/2209.07592 (2022) - [i64]Sahil Singla, Soheil Feizi:
Improved techniques for deterministic l2 robustness. CoRR abs/2211.08453 (2022) - [i63]Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri, Soheil Feizi:
Data-Centric Debugging: mitigating model failures via targeted data collection. CoRR abs/2211.09859 (2022) - [i62]Priyatham Kattakinda, Alexander Levine, Soheil Feizi:
Invariant Learning via Diffusion Dreamed Distribution Shifts. CoRR abs/2211.10370 (2022) - [i61]Sriram Balasubramanian, Soheil Feizi:
Towards Better Input Masking for Convolutional Neural Networks. CoRR abs/2211.14646 (2022) - [i60]Mazda Moayeri, Wenxiao Wang, Sahil Singla, Soheil Feizi:
Spuriosity Rankings: Sorting Data for Spurious Correlation Robustness. CoRR abs/2212.02648 (2022) - 2021
- [c51]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. AAAI 2021: 8038-8046 - [c50]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. AISTATS 2021: 3709-3717 - [c49]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. FAccT 2021: 466-477 - [c48]Mazda Moayeri, Soheil Feizi:
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. ICCV 2021: 7657-7666 - [c47]Vasu Singla, Sahil Singla, Soheil Feizi, David Jacobs:
Low Curvature Activations Reduce Overfitting in Adversarial Training. ICCV 2021: 16403-16413 - [c46]Alexander Levine, Soheil Feizi:
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks. ICLR 2021 - [c45]Sahil Singla, Soheil Feizi:
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers. ICLR 2021 - [c44]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c43]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. ICLR 2021 - [c42]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. ICLR 2021 - [c41]Alexander Levine, Soheil Feizi:
Improved, Deterministic Smoothing for L1 Certified Robustness. ICML 2021: 6254-6264 - [c40]Sahil Singla, Soheil Feizi:
Skew Orthogonal Convolutions. ICML 2021: 9756-9766 - [c39]Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi:
Improving Deep Learning Interpretability by Saliency Guided Training. NeurIPS 2021: 26726-26739 - [c38]Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Unsupervised anomaly detection with adversarial mirrored autoencoders. UAI 2021: 365-375 - [i59]Vasu Singla, Sahil Singla, David Jacobs, Soheil Feizi:
Low Curvature Activations Reduce Overfitting in Adversarial Training. CoRR abs/2102.07861 (2021) - [i58]Alexander Levine, Soheil Feizi:
Improved, Deterministic Smoothing for L1 Certified Robustness. CoRR abs/2103.10834 (2021) - [i57]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Overparameterization in Generative Adversarial Networks. CoRR abs/2104.05605 (2021) - [i56]Sahil Singla, Soheil Feizi:
Skew Orthogonal Convolutions. CoRR abs/2105.11417 (2021) - [i55]Aounon Kumar, Alexander Levine, Soheil Feizi:
Policy Smoothing for Provably Robust Reinforcement Learning. CoRR abs/2106.11420 (2021) - [i54]Sahil Singla, Surbhi Singla, Soheil Feizi:
Householder Activations for Provable Robustness against Adversarial Attacks. CoRR abs/2108.04062 (2021) - [i53]Mazda Moayeri, Soheil Feizi:
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. CoRR abs/2108.13797 (2021) - [i52]Priyatham Kattakinda, Soheil Feizi:
FOCUS: Familiar Objects in Common and Uncommon Settings. CoRR abs/2110.03804 (2021) - [i51]Sahil Singla, Soheil Feizi:
Causal ImageNet: How to discover spurious features in Deep Learning? CoRR abs/2110.04301 (2021) - [i50]Samyadeep Basu, Amr Sharaf, Nicolò Fusi, Soheil Feizi:
On Hard Episodes in Meta-Learning. CoRR abs/2110.11190 (2021) - [i49]Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi:
Improving Deep Learning Interpretability by Saliency Guided Training. CoRR abs/2111.14338 (2021) - [i48]Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi:
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection. CoRR abs/2112.04532 (2021) - [i47]Jiang Liu, Chun Pong Lau, Hossein Souri, Soheil Feizi, Rama Chellappa:
Mutual Adversarial Training: Learning together is better than going alone. CoRR abs/2112.05005 (2021) - [i46]Chun Pong Lau, Jiang Liu, Hossein Souri, Wei-An Lin, Soheil Feizi, Rama Chellappa:
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses. CoRR abs/2112.06323 (2021) - 2020
- [j10]Soheil Feizi, Farzan Farnia, Tony Ginart, David Tse:
Understanding GANs in the LQG Setting: Formulation, Generalization and Stability. IEEE J. Sel. Areas Inf. Theory 1(1): 304-311 (2020) - [j9]Soheil Feizi, Gerald T. Quon, Mariana Recamonde Mendoza, Muriel Médard, Manolis Kellis, Ali Jadbabaie:
Spectral Alignment of Graphs. IEEE Trans. Netw. Sci. Eng. 7(3): 1182-1197 (2020) - [c37]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c36]Alexander Levine, Soheil Feizi:
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. AAAI 2020: 4585-4593 - [c35]Luke J. O'Connor, Muriel Médard, Soheil Feizi:
Maximum Likelihood Embedding of Logistic Random Dot Product Graphs. AAAI 2020: 5289-5297 - [c34]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. AISTATS 2020: 3795-3805 - [c33]Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. AISTATS 2020: 3938-3947 - [c32]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c31]Samyadeep Basu, Xuchen You, Soheil Feizi:
On Second-Order Group Influence Functions for Black-Box Predictions. ICML 2020: 715-724 - [c30]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. ICML 2020: 5458-5467 - [c29]Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. ICML 2020: 8981-8991 - [c28]Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. NeurIPS 2020 - [c27]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. NeurIPS 2020 - [c26]Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. NeurIPS 2020 - [c25]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. NeurIPS 2020 - [c24]Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. NeurIPS 2020 - [i45]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. CoRR abs/2002.03239 (2020) - [i44]Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. CoRR abs/2002.10733 (2020) - [i43]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs. CoRR abs/2003.00652 (2020) - [i42]Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection. CoRR abs/2003.10713 (2020) - [i41]Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. CoRR abs/2006.00731 (2020) - [i40]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. CoRR abs/2006.12621 (2020) - [i39]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. CoRR abs/2006.12655 (2020) - [i38]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. CoRR abs/2006.14651 (2020) - [i37]Alexander Levine, Soheil Feizi:
Deep Partition Aggregation: Provable Defense against General Poisoning Attacks. CoRR abs/2006.14768 (2020) - [i36]Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. CoRR abs/2009.02470 (2020) - [i35]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. CoRR abs/2009.08061 (2020) - [i34]Pirazh Khorramshahi, Hossein Souri, Rama Chellappa, Soheil Feizi:
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue. CoRR abs/2009.11921 (2020) - [i33]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. CoRR abs/2010.02350 (2020) - [i32]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. CoRR abs/2010.05862 (2020) - [i31]Alexander Levine, Aounon Kumar, Thomas A. Goldstein, Soheil Feizi:
Tight Second-Order Certificates for Randomized Smoothing. CoRR abs/2010.10549 (2020) - [i30]Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. CoRR abs/2010.13924 (2020)
2010 – 2019
- 2019
- [j8]Soheil Feizi, Muriel Médard, Gerald T. Quon, Manolis Kellis, Ken Duffy:
Network Infusion to Infer Information Sources in Networks. IEEE Trans. Netw. Sci. Eng. 6(3): 402-417 (2019) - [c23]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation. ICCV 2019: 6499-6507 - [c22]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? ICLR (Poster) 2019 - [c21]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. ICML 2019: 414-423 - [c20]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. ICML 2019: 5848-5856 - [c19]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. NeurIPS 2019: 6778-6789 - [c18]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. NeurIPS 2019: 10408-10418 - [c17]Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi:
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. NeurIPS 2019: 10813-10823 - [i29]Angeline Aguinaldo, Ping-Yeh Chiang, Alexander Gain, Ameya Patil, Kolten Pearson, Soheil Feizi:
Compressing GANs using Knowledge Distillation. CoRR abs/1902.00159 (2019) - [i28]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. CoRR abs/1902.00407 (2019) - [i27]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation. CoRR abs/1902.00415 (2019) - [i26]Sahil Singla, Soheil Feizi:
Robustness Certificates Against Adversarial Examples for ReLU Networks. CoRR abs/1902.01235 (2019) - [i25]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. CoRR abs/1905.09747 (2019) - [i24]Alexander Levine, Sahil Singla, Soheil Feizi:
Certifiably Robust Interpretation in Deep Learning. CoRR abs/1905.12105 (2019) - [i23]Samuel Barham, Soheil Feizi:
Interpretable Adversarial Training for Text. CoRR abs/1905.12864 (2019) - [i22]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. CoRR abs/1906.00001 (2019) - [i21]Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Strong Baseline Defenses Against Clean-Label Poisoning Attacks. CoRR abs/1909.13374 (2019) - [i20]Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. CoRR abs/1910.10783 (2019) - [i19]Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi:
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. CoRR abs/1910.12370 (2019) - [i18]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. CoRR abs/1911.00111 (2019) - [i17]Samyadeep Basu, Xuchen You, Soheil Feizi:
Second-Order Group Influence Functions for Black-Box Predictions. CoRR abs/1911.00418 (2019) - [i16]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. CoRR abs/1911.08654 (2019) - [i15]Alexander Levine, Soheil Feizi:
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. CoRR abs/1911.09272 (2019) - [i14]Sahil Singla, Soheil Feizi:
Bounding Singular Values of Convolution Layers. CoRR abs/1911.10258 (2019) - [i13]Cassidy Laidlaw, Soheil Feizi:
Playing it Safe: Adversarial Robustness with an Abstain Option. CoRR abs/1911.11253 (2019) - 2018
- [c16]Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse:
Porcupine Neural Networks: Approximating Neural Network Landscapes. NeurIPS 2018: 4836-4846 - [i12]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? CoRR abs/1809.02104 (2018) - [i11]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. CoRR abs/1810.04147 (2018) - 2017
- [j7]Soheil Feizi, Ali Makhdoumi, Ken Duffy, Manolis Kellis, Muriel Médard:
Network Maximal Correlation. IEEE Trans. Netw. Sci. Eng. 4(4): 229-247 (2017) - [c15]Saeed Haghiri, Ali Nemati, Soheil Feizi, Amirali Amirsoleimani, Arash Ahmadi, Majid Ahmadi:
A memristor based binary multiplier. CCECE 2017: 1-4 - [c14]Soheil Feizi, Hamid Javadi, David Tse:
Tensor Biclustering. NIPS 2017: 1311-1320 - [i10]Soheil Feizi, David Tse:
Maximally Correlated Principal Component Analysis. CoRR abs/1702.05471 (2017) - [i9]Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse:
Porcupine Neural Networks: (Almost) All Local Optima are Global. CoRR abs/1710.02196 (2017) - [i8]Soheil Feizi, Changho Suh, Fei Xia, David Tse:
Understanding GANs: the LQG Setting. CoRR abs/1710.10793 (2017) - 2016
- [b1]Soheil Feizi-Khankandi:
On the analysis of complex networks: fundamental limits, scalable algorithms, and applications. Massachusetts Institute of Technology, Cambridge, USA, 2016 - [i7]Soheil Feizi, Gerald T. Quon, Mariana Recamonde Mendoza, Muriel Médard, Manolis Kellis, Ali Jadbabaie:
Spectral Alignment of Networks. CoRR abs/1602.04181 (2016) - [i6]Soheil Feizi, Muriel Médard, Gerald T. Quon, Manolis Kellis, Ken R. Duffy:
Network Infusion to Infer Information Sources in Networks. CoRR abs/1606.07383 (2016) - 2015
- [j6]Roadmap Epigenomics Consortium, Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi Moussavi, Pouya Kheradpour, ZhiZhuo Zhang, Jianrong Wang, Michael J. Ziller, Viren Amin, John W. Whitaker, Matthew D. Schultz, Lucas D. Ward, Abhishek Sarkar, Gerald T. Quon, Richard S. Sandstrom, Matthew L. Eaton, Yi-Chieh Wu, Andreas R. Pfenning, Xinchen Wang, Melina Claussnitzer, Yaping Liu, Cristian Coarfa, R. Alan Harris, Noam Shoresh, Charles B. Epstein, Elizabeta Gjoneska, Danny Leung, Wei Xie, R. David Hawkins, Ryan Lister, Chibo Hong, Philippe Gascard, Andrew J. Mungall, Richard A. Moore, Eric Chuah, Angela Tam, Theresa K. Canfield, R. Scott Hansen, Rajinder Kaul, Peter J. Sabo, Mukul S. Bansal, Annaick Carles, Jesse R. Dixon, Kyle Kai-How Farh, Soheil Feizi, Rosa Karlic, Ah-Ram Kim, Ashwinikumar Kulkarni, Daofeng Li, Rebecca F. Lowdon, GiNell Elliott, Tim R. Mercer, Shane J. Neph, Vitor Onuchic, Paz Polak, Nisha Rajagopal, Pradipta Ray, Richard C. Sallari, Kyle T. Siebenthall, Nicholas A. Sinnott-Armstrong, Michael Stevens, Robert E. Thurman, Jie Wu, Bo Zhang, Xin Zhou, Arthur E. Beaudet, Laurie A. Boyer, Philip L. De Jager, Peggy J. Farnham, Susan J. Fisher, David Haussler, Steven J. M. Jones, Wei Li, Marco A. Marra, Michael T. McManus, Shamil R. Sunyaev, James A. Thomson, Thea D. Tlsty, Li-Huei Tsai, Wei Wang, Robert A. Waterland, Michael Q. Zhang, Lisa H. Chadwick, Bradley E. Bernstein, Joseph F. Costello, Joseph R. Ecker, Martin Hirst, Alexander Meissner, Aleksandar Milosavljevic, Bing Ren, John A. Stamatoyannopoulos, Ting Wang, Manolis Kellis:
Integrative analysis of 111 reference human epigenomes Open. Nat. 518(7539): 317-330 (2015) - [i5]Jeffrey G. Andrews, Alexandros G. Dimakis, Lara Dolecek, Michelle Effros, Muriel Médard, Olgica Milenkovic, Andrea Montanari, Sriram Vishwanath, Edmund M. Yeh, Randall Berry, Ken R. Duffy, Soheil Feizi, Saul Kato, Manolis Kellis, Stuart Licht, Jon Sorenson, Lav R. Varshney, Haris Vikalo:
A Perspective on Future Research Directions in Information Theory. CoRR abs/1507.05941 (2015) - 2014
- [j5]Soheil Feizi, Muriel Médard:
On Network Functional Compression. IEEE Trans. Inf. Theory 60(9): 5387-5401 (2014) - [j4]Soheil Feizi, Georgios Angelopoulos, Vivek K. Goyal, Muriel Médard:
Backward Adaptation for Power Efficient Sampling. IEEE Trans. Signal Process. 62(16): 4327-4338 (2014) - [c13]Soheil Feizi, Daniel E. Lucani, Chres W. Sørensen, Ali Makhdoumi, Muriel Médard:
Tunable sparse network coding for multicast networks. NetCod 2014: 1-6 - [c12]Luke J. O'Connor, Soheil Feizi:
Biclustering Usinig Message Passing. NIPS 2014: 3617-3625 - 2012
- [j3]Soheil Feizi, Vivek K. Goyal, Muriel Médard:
Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals. IEEE Trans. Signal Process. 60(10): 5440-5450 (2012) - [c11]Muriel L. Rambeloarison, Soheil Feizi, Georgios Angelopoulos, Muriel Médard:
Empirical rate-distortion study of compressive sensing-based joint source-channel coding. ACSCC 2012: 1224-1228 - [c10]Soheil Feizi, Vivek K. Goyal, Muriel Médard:
Time-stampless adaptive nonuniform sampling for stochastic signals. ICASSP 2012: 3809-3812 - 2011
- [c9]Soheil Feizi, Muriel Médard:
A power efficient sensing/communication scheme: Joint source-channel-network coding by using compressive sensing. Allerton 2011: 1048-1054 - [i4]Soheil Feizi-Khankandi, Muriel Médard:
A Power Efficient Sensing/Communication Scheme: Joint Source-Channel-Network Coding by Using Compressive Sensing. CoRR abs/1110.0428 (2011) - [i3]Soheil Feizi-Khankandi, Vivek K. Goyal, Muriel Médard:
Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals. CoRR abs/1110.3774 (2011) - 2010
- [c8]Soheil Feizi, Vivek K. Goyal, Muriel Médard:
Locally Adaptive Sampling. Allerton 2010: 152-159 - [c7]Soheil Feizi, Muriel Médard, Michelle Effros:
Compressive sensing over networks. Allerton 2010: 1129-1136 - [c6]Soheil Feizi, Muriel Médard:
Cases where finding the minimum entropy coloring of a characteristic graph is a polynomial time problem. ISIT 2010: 116-120 - [i2]Soheil Feizi-Khankandi, Muriel Médard:
On Network Functional Compression. CoRR abs/1011.5496 (2010) - [i1]Soheil Feizi-Khankandi, Muriel Médard, Michelle Effros:
Compressive Sensing Over Networks. CoRR abs/1012.0955 (2010)
2000 – 2009
- 2009
- [j2]Sina Zahedpour, Soheil Feizi, Arash Amini, Mahmoud Ferdosizadeh Naeiny, Farrokh Marvasti:
Impulsive Noise Cancellation Based on Soft Decision and Recursion. IEEE Trans. Instrum. Meas. 58(8): 2780-2790 (2009) - [j1]Mohammad Ali Akhaee, Mohammad Javad Saberian, Soheil Feizi, Farokh Marvasti:
Robust Audio Data Hiding Using Correlated Quantization With Histogram-Based Detector. IEEE Trans. Multim. 11(5): 834-842 (2009) - [c5]Soheil Feizi, Muriel Médard:
When do only sources need to compute? On functional compression in tree networks. Allerton 2009: 447-454 - [c4]Soheil Feizi-Khankandi, Muriel Médard:
Multi-Functional Compression with Side Information. GLOBECOM 2009: 1-5 - 2008
- [c3]Soheil Feizi, Sina Zahedpour, Mahdi Soltanolkotabi, Arash Amini, Farokh Marvasti:
Salt and pepper noise removal for image signals. ICT 2008: 1-5 - [c2]Sina Zahedpour, Soheil Feizi, Arash Amini, Mahmoud Ferdosizadeh Naeiny, Farokh Marvasti:
Impulsive noise cancellation using CFAR and iterative techniques. ICT 2008: 1-5 - [c1]Soheil Feizi-Khankandi, Farid Ashtiani:
Lower and Upper Bounds for Throughput Capacity of a Cognitive Ad Hoc Network Overlaid on a Cellular Network. WCNC 2008: 2759-2764
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-05-24 01:03 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
