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.

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Optimization Algorithms

1. One-Sided Matrix Completion from Ultra-Sparse Samples
   H. R. Zhang, Z. Zhang, H. L. Nguyen, and G. Lan
   Transactions on Machine Learning Research (TMLR), 2026. Featured Certification
   

2. 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
   arXiv preprint
   

3. 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
   

Machine Learning Theory

1. 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
   

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

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

4. On the Generalization Effects of Linear Transformations in Data Augmentation
   Sen Wu*, H. R. Zhang*, Gregory Valiant, and Christopher Ré
   International Conference on Machine Learning (ICML) 2020
   

5. Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
   Yuanzhi Li, Tengyu Ma, and H. R. Zhang*
   Conference on Learning Theory (COLT) 2020
   

6. Recovery Guarantees for Quadratic Tensors with Sparse Observations
   H. R. Zhang, Vatsal Sharan, Moses Charikar, and Yingyu Liang
   Artificial Intelligence and Statistics (AISTATS) 2019

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

Neural Networks and Generative Models

1. 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

2. 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
   

3. 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
   

4. 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
   

5. 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
   

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

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

8. Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
   Dongyue Li, Haotian Ju, Aneesh Sharma, and H. R. Zhang
   SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023

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

10. Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
    Michael Zhang, Nimit Sohoni, H. R. Zhang, Chelsea Finn, and Christopher Ré
    International Conference on Machine Learning (ICML) 2022. Long presentation
    

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

12. Observational Supervision for Medical Image Classification using Gaze Data
    Khaled Saab, Sarah Hooper, Nimit Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared Dunnmon, H. R. Zhang, Daniel Rubin, and Christopher Ré
    Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021
    

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

Social Networks and Network Data

1. 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)
   arXiv preprint
   

2. Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
   Abhinav Nippani, Dongyue Li, Haotian Ju, Haris N. Koutsopoulos, H. R. Zhang
   NeurIPS 2023, Datasets and Benchmarks Track

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

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

5. Approximate Personalized PageRank on Dynamic Graphs
   H. R. Zhang, Peter Lofgren, and Ashish Goel
   KDD 2016. Oral presentation
   [code]

6. A Note on Modeling Retweet Cascades on Twitter
   Ashish Goel, Kamesh Munagala, Aneesh Sharma, and H. R. Zhang*
   Workshop on Algorithms and Models for the Web Graph (WAW) 2015

7. Connectivity in Random Forests and Credit Networks
   Ashish Goel, Sanjeev Khanna, Sharath Raghvendra, and H. R. Zhang*
   Symposium on Discrete Algorithms (SODA) 2015

Algorithmic Game Theory

1. Incentive Ratio: A Game Theoretical Analysis of Market Equilibria
   Ning Chen, Xiaotie Deng, Bo Tang, H. R. Zhang, and Jie Zhang
   Information and Computation, 2022

2. Incentives for Strategic Behavior in Fisher Market Games
   Ning Chen, Xiaotie Deng, Bo Tang, and H. R. Zhang*
   AAAI 2016

3. Computing the Nucleolus of Matching, Cover and Clique Games
   Ning Chen, Pinyan Lu, and H. R. Zhang*
   AAAI 2012. Oral presentation

4. Incentive Ratios of Fisher Markets
   Ning Chen, Xiaotie Deng, H. R. Zhang*, and Jie Zhang
   International Colloquium on Automata, Languages, and Programming (ICALP) 2012

5. On Strategy-proof Allocation without Payments or Priors
   Li Han, Chunzhi Su, Linpeng Tang, and H. R. Zhang*
   Conference on Web and Internet Economics (WINE) 2011