Link to DBLP. You can find the code for reproducing our empirical work in the following GitHub. Acknowledgement: Our research has been generously supported by funds from the National Science Foundation and from JP Morgan Chase.

Manuscripts

• One-Sided Matrix Completion from Ultra-Sparse Samples. H. R. Zhang, Z. Zhang, H. L. Nguyen, and G. Lan.

2025

• Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning. D. Li, Z. Zhang, M. Duan, E. Dobriban, and H. R. Zhang. To appear in KDD, 2026

• Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation. Z. Zhang, M. Duan, H. N.Koutsopoulos, and H. R. Zhang. To appear in KDD, 2026 (Datasets)

• Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation. Z. Zhang, M. Duan, Y. Ye, and H. R. Zhang. To appear in AAAI, 2026

• Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation. Z. Zhang*, Z. Zhang*, D. Li, L. Wang, J. Dy, and H. R. Zhang. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025

• Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers. F. Yang*, H. R. Zhang*, S. Wu, C. Ré, and W. Su. Journal of Machine Learning Research (JMLR), 2025

• Efficient Ensemble for Fine-tuning Language Models on Multiple Datasets. D. Li, Z. Zhang, L. Wang, and H. R. Zhang. Association for Computational Linguistics (ACL), 2025

2024

• 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. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024
  

• Learning Tree-Structured Composition of Data Augmentation. D. Li, K. Chen, P. Radivojac, and H. R. Zhang. Transactions on Machine Learning Research (TMLR), 2024
  

2023

• 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. Neural Information Processing Systems (NeurIPS Datasets Track), 2023

• Improved Group Robustness via Classifier Retraining on Independent Splits. T. H. Nguyen, H. R. Zhang, and H. L. Nguyen. Transactions on Machine Learning Research (TMLR), 2023

• Boosting Multitask Learning on Graphs through Higher-Order Task Affinities. D. Li, H. Ju, A. Sharma, and H. R. Zhang. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023

• Identification of Negative Transfers in Multitask Learning Using Surrogate Models. D. Li, H. L. Nguyen, and H. R. Zhang. Transactions on Machine Learning Research (TMLR), 2023. Featured Certification

• Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion. H. Ju, D. Li, A. Sharma, and H. R. Zhang. Artificial Intelligence and Statistics (AISTATS), 2023

• Optimal Intervention on Weighted Networks via Edge Centrality. D. Li, T. Eliassi-Rad, and H. R. Zhang. SIAM International Conference on Data Mining (SDM), 2023

2022

• Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees. H. Ju, D. Li, and H. R. Zhang. International Conference on Machine Learning (ICML), 2022

• Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations. M. Zhang, N. Sohoni, H. R. Zhang, C. Finn, and C. Ré. International Conference on Machine Learning (ICML), 2022. 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, 2022

2021

• Improved Regularization and Robustness for Fine-tuning in Neural Networks. D. Li and H. R. Zhang. Neural Information Processing Systems (NeurIPS), 2021

• Observational Supervision for Medical Image Classification using Gaze Data. K. Saab, S. Hooper, N. Sohoni, J. Parmar, B. Pogatchnik, S. Wu, J. Dunnmon, H. R. Zhang, D. Rubin, and C. Ré. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021

2020

• On the Generalization Effects of Linear Transformations in Data Augmentation. S. Wu*, H. R. Zhang*, G. Valiant, and C. Ré. International Conference on Machine Learning (ICML), 2020

• Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK. Y. Li*, T. Ma*, and H. R. Zhang*. Annual Conference on Learning Theory (COLT), 2020

• Understanding and Improving Information Transfer in Multi-Task Learning. S. Wu*, H. R. Zhang*, and C. Ré. International Conference on Learning Representations (ICLR), 2020
  

Prior to 2019

• Algorithms and Generalization for Large-scale Matrices and Tensors. Thesis Ph.D. Stanford University, 2019

• Pruning based Distance Sketches with Provable Guarantees on Random Graphs. H. Zhang, H. Yu, and A. Goel. The Web Conference (WWW) 2019. Oral presentation. [code]

• Recovery Guarantees for Quadratic Tensors with Sparse Observations. H. Zhang, V. Sharan, M. Charikar, and Y. Liang. Artificial Intelligence and Statistics (AISTATS), 2019

• Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations. Y. Li*, T. Ma*, and H. Zhang*. Conference on Learning Theory (COLT) 2018. Best Paper Award

• Approximate Personalized PageRank on Dynamic Graphs. H. Zhang, P. Lofgren, and A. Goel. KDD 2016. Oral presentation. [code]

• Incentives for Strategic Behavior in Fisher Market Games. N. Chen*, X. Deng*, B. Tang*, and H. Zhang*. AAAI, 2016

• A Note on Modeling Retweet Cascades on Twitter. A. Goel*, K. Munagala*, A. Sharma*, and H. Zhang*. Workshop on Algorithms and Models for the Web Graph (WAW), 2015

• Connectivity in Random Forests and Credit Networks. A. Goel*, S. Khanna*, S. Raghvendra*, and H. Zhang*. Symposium on Discrete Algorithms (SODA), 2015

• Computing the Nucleolus of Matching, Cover and Clique Games. N. Chen*, P. Lu*, and H. Zhang*. AAAI, 2012. Oral presentation

• Incentive Ratios of Fisher Markets. N. Chen*, X. Deng*, H. Zhang*, and J. Zhang*. International Colloquium on Automata, Languages, and Programming (ICALP), 2012

• Fixed-Parameter Tractability of almost CSP Problem with Decisive Relations. C. Zhang and H. Zhang. FAW-AAIM, 2012

• On Strategy-proof Allocation without Payments or Priors. L. Han*, C. Su*, L. Tang*, and H. Zhang*. Conference on Web and Internet Economics (WINE), 2011

Remarks:

• The papers above that are marked by stars follow the convention of alphabetical ordering of author names.

• Research description (Work in progress).