Hongyang Zhang
Orcid: 0000-0002-0548-6068Affiliations:
- University of Waterloo, David R. Cheriton School of Computer Science, Canada
- Toyota Technological Institute at Chicago, USA (former)
- Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh, PA, USA (PhD 2019)
- Peking University, School of Electronics Engineering and Computer Science, MOE, Key Laboratory of Machine Perception, Beijing, China (former)
According to our database1,
Hongyang Zhang
authored at least 67 papers
between 2013 and 2025.
Collaborative distances:
Collaborative distances:
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Bibliography
2025
IEEE Trans. Inf. Forensics Secur., 2025
2024
IEEE Trans. Pattern Anal. Mach. Intell., March, 2024
Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions.
CoRR, 2024
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
IEEE Trans. Inf. Theory, October, 2023
IACR Cryptol. ePrint Arch., 2023
CoRR, 2023
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022
2021
Proceedings of the Mathematical and Scientific Machine Learning, 2021
2020
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.
J. Mach. Learn. Res., 2020
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness.
CoRR, 2020
CoRR, 2020
Random Smoothing Might be Unable to Certify 𝓁<sub>∞</sub> Robustness for High-Dimensional Images.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Computer Vision - ECCV 2020, 2020
2019
New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications.
PhD thesis, 2019
J. Mach. Learn. Res., 2019
CoRR, 2019
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
2018
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures.
CoRR, 2018
Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018
Proceedings of the 45th International Colloquium on Automata, Languages, and Programming, 2018
2017
S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms.
CoRR, 2017
Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s<sub>2</sub>-Regularization.
CoRR, 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017
2016
Completing Low-Rank Matrices With Corrupted Samples From Few Coefficients in General Basis.
IEEE Trans. Inf. Theory, 2016
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
Proceedings of the 29th Conference on Learning Theory, 2016
2015
Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification.
Proceedings of the British Machine Vision Conference 2015, 2015
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
2014
2013
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013