See also DBLP for further bibliographic information.
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach. H. R. Zhang, D. Li, and H. Ju. Transactions on Machine Learning Research (TMLR), 2024
Scalable Fine-tuning From Multiple Data Sources: A First-order Approximation Approach. D. Li, Z. Zhang, L. Wang, and H. R. Zhang. Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP), 2024
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity. D. Li, A. Sharma, and H. R. Zhang. KDD ’24
Learning Tree-Structured Composition of Data Augmentation. D. Li, K. Chen, P. Radivojac, and H. R. Zhang. TMLR ’24
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis. A. Nippani, D. Li, H. Ju, H. N. Koutsopoulos, and H. R. Zhang. NeurIPS ’23 Data
Improved Group Robustness via Classifier Retraining on Independent Splits. T. H. Nguyen, H. R. Zhang, and H. L. Nguyen. TMLR ’23
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities. D. Li, H. Ju, A. Sharma, and H. R. Zhang. KDD ’23
Identification of Negative Transfers in Multitask Learning Using Surrogate Models. D. Li, H. L. Nguyen, and H. R. Zhang. TMLR ’23. Featured Certification
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion. H. Ju, D. Li, A. Sharma, and H. R. Zhang. AISTATS ’23
Optimal Intervention on Weighted Networks via Edge Centrality. D. Li, T. Eliassi-Rad, and H. R. Zhang. SDM ’23
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees. H. Ju, D. Li, and H. R. Zhang. ICML ’22
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations. M. Zhang, N. Sohoni, H. R. Zhang, C. Finn, and C. Ré. ICML ’22. Long presentation
Incentive Ratio: A Game Theoretical Analysis of Market Equilibria. N. Chen, X. Deng, B. Tang, H. R. Zhang, and J. Zhang. Information and Computation ’22
Improved Regularization and Robustness for Fine-tuning in Neural Networks. D. Li and H. R. Zhang. NeurIPS ’21
Observational Supervision for Medical Image Classification using Gaze Data. K. Saab, S. Hooper, N. Sohoni, J. Dunnmon, H. R. Zhang, D. Rubin, and C. Ré. MICCAI ’21
On the Generalization Effects of Linear Transformations in Data Augmentation. S. Wu*, H. R. Zhang*, G. Valiant, and C. Ré. ICML ’20
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK. Y. Li*, T. Ma*, and H. R. Zhang*. COLT ’20
Understanding and Improving Information Transfer in Multi-Task Learning. S. Wu*, H. R. Zhang*, and C. Ré. ICLR ’20
Algorithms and Generalization for Large-scale Matrices and Tensors. H. Zhang. Ph.D. Thesis ’19, Stanford University
Pruning based Distance Sketches with Provable Guarantees on Random Graphs. H. Zhang, H. Yu, and A. Goel. WWW ’19. Oral presentation
Recovery Guarantees for Quadratic Tensors with Sparse Observations. H. Zhang, V. Sharan, M. Charikar, and Y. Liang. AISTATS ’19
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations. Y. Li*, T. Ma*, and H. Zhang*. COLT ’18. Best Paper Award
Approximate Personalized PageRank on Dynamic Graphs. H. Zhang, P. Lofgren, and A. Goel. KDD ’16. Oral presentation
Incentives for Strategic Behavior in Fisher Market Games. N. Chen*, X. Deng*, B. Tang*, and H. Zhang*. AAAI ’16
A Note on Modeling Retweet Cascades on Twitter. A. Goel*, K. Munagala*, A. Sharma*, and H. Zhang*. WAW ’15
Connectivity in Random Forests and Credit Networks. A. Goel*, S. Khanna*, S. Raghvendra*, and H. Zhang*. SODA ’15
Computing the Nucleolus of Matching, Cover and Clique Games. N. Chen*, P. Lu*, and H. Zhang*. AAAI ’12. Oral presentation
Incentive Ratios of Fisher Markets. N. Chen*, X. Deng*, H. Zhang*, and J. Zhang*. ICALP ’12
Fixed-Parameter Tractability of almost CSP Problem with Decisive Relations. C. Zhang* and H. Zhang*. FAW-AAIM ’12
On Strategy-proof Allocation without Payments or Priors. L. Han*, C. Su*, L. Tang*, and H. Zhang*. WINE ’11