Hongyang Zhang

Orcid: 0000-0002-0548-6068

Affiliations:
  • 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 (former)
  • 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 62 papers between 2013 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2024

AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls.
CoRR, 2024

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.
CoRR, 2024

Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Recovery From Non-Decomposable Distance Oracles.
IEEE Trans. Inf. Theory, October, 2023

An Analysis of Robustness of Non-Lipschitz Networks.
J. Mach. Learn. Res., 2023

zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training.
IACR Cryptol. ePrint Arch., 2023

Unbiased Watermark for Large Language Models.
CoRR, 2023

DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models.
CoRR, 2023

RAIN: Your Language Models Can Align Themselves without Finetuning.
CoRR, 2023

Investigating the Existence of "Secret Language" in Language Models.
CoRR, 2023

A Law of Robustness beyond Isoperimetry.
Proceedings of the International Conference on Machine Learning, 2023

Understanding the Impact of Adversarial Robustness on Accuracy Disparity.
Proceedings of the International Conference on Machine Learning, 2023

Causal Balancing for Domain Generalization.
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
Direct-Effect Risk Minimization for Domain Generalization.
CoRR, 2022

Towards Robust Dataset Learning.
CoRR, 2022

A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition.
CoRR, 2022

How Many Data Are Needed for Robust Learning?
CoRR, 2022

Towards Transferable Unrestricted Adversarial Examples with Minimum Changes.
CoRR, 2022

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Building Robust Ensembles via Margin Boosting.
Proceedings of the International Conference on Machine Learning, 2022

RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval.
Proceedings of the International Conference on Machine Learning, 2022

Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021
Unrestricted Adversarial Attacks on ImageNet Competition.
CoRR, 2021

Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients.
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

Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients.
CoRR, 2020

Adversarial Robustness Through Local Lipschitzness.
CoRR, 2020

Random Smoothing Might be Unable to Certify 𝓁<sub>∞</sub> Robustness for High-Dimensional Images.
CoRR, 2020

A Closer Look at Accuracy vs. Robustness.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Self-Adaptive Training: beyond Empirical Risk Minimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Design and Interpretation of Universal Adversarial Patches in Face Detection.
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

Non-Convex Matrix Completion and Related Problems via Strong Duality.
J. Mach. Learn. Res., 2019

Design and Interpretation of Universal Adversarial Patches in Face Detection.
CoRR, 2019

Testing Matrix Rank, Optimally.
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Efficient Symmetric Norm Regression via Linear Sketching.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Theoretically Principled Trade-off between Robustness and Accuracy.
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
On the Applications of Robust PCA in Image and Video Processing.
Proc. IEEE, 2018

Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures.
CoRR, 2018

Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex.
CoRR, 2018

Matrix Completion and Related Problems via Strong Duality.
Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

Improved Algorithms for Adaptive Compressed Sensing.
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

Noise-Tolerant Interactive Learning Using Pairwise Comparisons.
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

Differentially Private Clustering in High-Dimensional Euclidean Spaces.
Proceedings of the 34th International Conference on Machine Learning, 2017

Fast Compressive Phase Retrieval under Bounded Noise.
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

Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning and 1-bit Compressed Sensing under Asymmetric Noise.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Relations Among Some Low-Rank Subspace Recovery Models.
Neural Comput., 2015

Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification.
Proceedings of the British Machine Vision Conference 2015, 2015

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Robust latent low rank representation for subspace clustering.
Neurocomputing, 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


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