Hongyang R. Zhang
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Selected papers
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Papers

By year By topic
Matrix recovery Neural net Hessian Multitask learning Language models Robustness and data augmentation Large-scale networks Incentives and cooperative games Others

Matrix recovery

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

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

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

Neural net Hessian

  1. WinQ: Accelerating Quantization-Aware Training of Language Models around Saddle Points
    D. Li, Z. Liu, K. Yi, Z. Zhang, C. Zhao, R. Krishnamoorthi, H. Khaitan, H. R. Zhang, and S. Li
    International Conference on Machine Learning (ICML), 2026

  2. 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

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

  4. 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

Multitask learning

  1. Efficient Estimation of Kernel Surrogate Models for Task Attribution
    Z. Zhang, M. Duan, and H. R. Zhang
    International Conference on Learning Representations (ICLR), 2026

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

  3. 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

  4. 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

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

  6. 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

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

Language models

  1. MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment
    D. Li, Z. Zhang, L. Wang, and H. R. Zhang
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2026

  2. Long-Context Demonstration Selection Using State Space Models
    Z. Zhang, Z. Zhang, R. Xiong, G. Cooperman, and H. R. Zhang
    Findings of Empirical Methods in Natural Language Processing (EMNLP Findings), 2026

  3. 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

  4. 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

  5. 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

Robustness and data augmentation

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

  2. 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

  3. 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

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

  5. 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

  6. 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

Large-scale networks

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

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

  3. Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
    A. Nippani, D. Li, H. Ju, H. N. Koutsopoulos, H. R. Zhang
    NeurIPS, 2023 (Datasets)

  4. 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

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

  6. Approximate Personalized PageRank on Dynamic Graphs
    H. R. Zhang, P. Lofgren, and A. Goel
    KDD 2016. Oral presentation

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

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

Incentives and cooperative games

  1. 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

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

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

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

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

Others

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