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Publications by Year

Manuscripts

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

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
  

• 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
  

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
  [blog: When Multi-Task Learning Works – And When It Doesn’t]

Prior to 2019

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

• 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

Remark: Some of the papers above follow alphabetical ordering of author names. They are marked by stars.