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Soheil Feizi-Khankandi
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2020 – today
- 2023
[i79]Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, Soheil Feizi:
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks. CoRR abs/2302.02300 (2023)- [i78]Wenxiao Wang, Soheil Feizi:
Temporal Robustness against Data Poisoning. CoRR abs/2302.03684 (2023) - [i77]Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi:
CUDA: Convolution-based Unlearnable Datasets. CoRR abs/2303.04278 (2023) - [i76]Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi:
Can AI-Generated Text be Reliably Detected? CoRR abs/2303.11156 (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) - [c56]Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. AISTATS 2022: 9908-9942 - [c55]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 - [c54]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 - [c53]Sahil Singla, Soheil Feizi:
Salient ImageNet: How to discover spurious features in Deep Learning? ICLR 2022 - [c52]Sahil Singla, Surbhi Singla, Soheil Feizi:
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100. ICLR 2022 - [c51]Aounon Kumar, Alexander Levine, Soheil Feizi:
Policy Smoothing for Provably Robust Reinforcement Learning. ICLR 2022 - [c50]Priyatham Kattakinda, Soheil Feizi:
FOCUS: Familiar Objects in Common and Uncommon Settings. ICML 2022: 10825-10847 - [c49]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. ICML 2022: 22769-22783 - [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
- [c48]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. AAAI 2021: 8038-8046 - [c47]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. AISTATS 2021: 3709-3717 - [c46]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. FAccT 2021: 466-477 - [c45]Mazda Moayeri, Soheil Feizi:
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. ICCV 2021: 7657-7666 - [c44]Vasu Singla, Sahil Singla, Soheil Feizi, David Jacobs:
Low Curvature Activations Reduce Overfitting in Adversarial Training. ICCV 2021: 16403-16413 - [c43]Alexander Levine, Soheil Feizi:
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks. ICLR 2021 - [c42]Sahil Singla, Soheil Feizi:
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers. ICLR 2021 - [c41]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c40]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. ICLR 2021 - [c39]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. ICLR 2021 - [c38]Alexander Levine, Soheil Feizi:
Improved, Deterministic Smoothing for L1 Certified Robustness. ICML 2021: 6254-6264 - [c37]Sahil Singla, Soheil Feizi:
Skew Orthogonal Convolutions. ICML 2021: 9756-9766 - [c36]Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi:
Improving Deep Learning Interpretability by Saliency Guided Training. NeurIPS 2021: 26726-26739 - [c35]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) - [c34]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c33]Alexander Levine, Soheil Feizi:
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. AAAI 2020: 4585-4593 - [c32]Luke J. O'Connor, Muriel Médard, Soheil Feizi:
Maximum Likelihood Embedding of Logistic Random Dot Product Graphs. AAAI 2020: 5289-5297 - [c31]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. AISTATS 2020: 3795-3805 - [c30]Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. AISTATS 2020: 3938-3947 - [c29]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 - [c28]Samyadeep Basu, Xuchen You, Soheil Feizi:
On Second-Order Group Influence Functions for Black-Box Predictions. ICML 2020: 715-724 - [c27]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. ICML 2020: 5458-5467 - [c26]Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. ICML 2020: 8981-8991 - [c25]Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. NeurIPS 2020 - [c24]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. NeurIPS 2020 - [c23]Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. NeurIPS 2020 - [c22]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. NeurIPS 2020 - [c21]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) - [c20]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation. ICCV 2019: 6499-6507 - [c19]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? ICLR (Poster) 2019 - [c18]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 - [c17]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 - [c16]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. NeurIPS 2019: 6778-6789 - [c15]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. NeurIPS 2019: 10408-10418 - [c14]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
- [c13]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) - [c12]Saeed Haghiri, Ali Nemati, Soheil Feizi, Amirali Amirsoleimani, Arash Ahmadi, Majid Ahmadi:
A memristor based binary multiplier. CCECE 2017: 1-4 - [c11]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]